Search Results (8,374)

With the advent of the big data era, traditional storage technologies struggle to meet the demands for long-term, secure, and cost-effective preservation of massive amounts of information. Collinear holographic storage technology has emerged as a strong contender for next-generation optical storage due to its high storage density, rapid parallel transmission, and exceptional reliability. Among various storage materials, phenanthraquinone-doped poly(methyl methacrylate) (PQ/PMMA) photopolymer has garnered significant attention for its negligible photo-induced volume shrinkage, low cost, controllable thickness, and polarization-sensitive holographic response properties. However, the material’s limited photosensitivity, low polarization response, and poor optical uniformity severely constrain its application in high-speed recording and multidimensional multiplexing holographic systems. This paper reviews the primary methods and strategies employed over the past five years to enhance the holographic performance of PQ/PMMA photopolymer materials, based on the microscopic physicochemical mechanisms underlying traditional and polarization holography, including chemical modification, nanoscale doping, mechanical control, etc. Through a systematic review of these research advances, this paper aims to provide theoretical foundations and technical references for developing high-performance PQ/PMMA photopolymer materials suitable for collinear holographic storage. Full article

The retrofitting of existing stadiums with retractable roof systems presents a complex interdisciplinary challenge, requiring the reconciliation of aged structural capacity with modern performance demands. This paper investigates the engineering design and analysis of a new retractable roof system for the Ningbo (Yinzhou) Tennis Center, a facility originally completed in 2007 and now requiring an upgrade to host higher-tier WTA 500 events. The retrofit is further complicated by increased seismic design requirements and the need to preserve the existing structure. To address these constraints, this study proposes a novel, structurally independent roof system comprising 12 radially deployable units supported by an external single-layer spatial grid and lambda-shaped columns. A multidisciplinary approach integrates structural engineering, mechanical systems, and architectural technology. Key innovations include (1) the selection and detailed modeling of a rack-and-pinion drive mechanism, with a floating engagement design to accommodate dynamic load transfer; (2) a two-stage analytical framework employing both sub-assembly and integrated assembly finite element models to capture the unique mechanical behavior and coupling effects between the new and existing structures; (3) the strategic implementation of circumferential hoop cables to counteract uplift forces and redirect the internal force distribution in the supporting bifurcated columns; and (4) the validation of structural integrity through comprehensive static, stability, and seismic gap analyses, informed by wind tunnel testing. The results demonstrate that the proposed system satisfies all strength, stiffness, and stability criteria under multiple operational states (open, closed, and transitional) and meets the enhanced seismic fortification standards. This research provides a validated theoretical foundation and practical implementation guidelines for this specific stadium retrofit, demonstrating a viable pathway for extending the service life of aging sports infrastructure, with insights that may inform similar urban renewal projects under comparable conditions. Full article

Spiking neural networks (SNNs) offer inherent advantages in processing temporal information. However, their network topologies are predominantly algorithm-generated, lacking constraints from biological brain connectivity, which limits their bio-plausibility. In our previous work, we constructed a spiking neural network (SNN) by incorporating the topological structure of functional brain networks derived from fMRI data of healthy subjects and proposed an fMRISNN model. This model was further employed as the reservoir layer of a liquid state machine (LSM) to build a speech recognition framework. In this framework, the Lyon ear model and the BSA were used to encode speech signals into spike sequences; however, this approach suffers from high computational cost and limited adaptability to temporal variations. To address these limitations, we propose an enhanced Mel-frequency cepstral coefficient (MFCC)-driven sparse spike encoding method. For the speech recognition task, we systematically compare the two preprocessing pipelines in terms of spike number, spike sparsity, encoding time, and downstream speech recognition performance. Experimental results show that the proposed method generates substantially fewer spikes, achieves markedly higher sparsity, and requires significantly less encoding time, while maintaining nearly the same recognition accuracy under the same LSM-based framework. These findings indicate that improved speech input representation can enhance the computational efficiency of SNN-based speech recognition without compromising recognition capability. In addition, the fMRISNN model significantly outperforms several baseline models with algorithmically generated topologies. Compared with mainstream models reported in the literature, although the deep convolutional neural network (CNN) still achieves higher absolute recognition accuracy, the fMRISNN exhibits clear advantages in terms of model parameter size and theoretical energy efficiency. Full article

Contemporary problems ranging from allergies, myopia, and obesity to chronic anxiety, loneliness, and ultralow fertility can be understood as consequences of evolutionary mismatch intensified by the polycrisis, in which accelerating technological and socioeconomic changes push human adaptations beyond what they evolved to handle. We sought to provide a conceptual review that maps these problems to adaptive needs that are disrupted in highly modernized environments. We then introduce the social evolutionary mismatch and competition hypothesis, which proposes that social aspects of evolutionary mismatch—e.g., increasing population sizes, fragmented communities, rising socioeconomic inequality, constant exposure to inflated social status cues—have a distinct effect of heightening both real and perceived competition. In turn, this perspective can help us make sense of predictable variation in psychosocial outcomes, including obsessive status pursuit, hostility, and social withdrawal. Finally, we outline strategies to lessen the impact of these dynamics by reducing sources of evolutionary mismatch. In sum, we contribute (1) an exposition of how the polycrisis exacerbates evolutionary mismatch and the adaptive needs that are impacted, (2) a theoretical advance identifying mismatch-driven competition as a predictor of multiple problematic outcomes, and (3) a translational framework showing how evolutionary insights can inform interventions to promote well-being in a time of profound societal strain. Full article

Cabled underwater information networks (CUINs) are a focal point and priority in the field of global marine science and technology. Reliability and economic viability are among the primary constraints on the large-scale deployment of such networks. The remote power supply system for grid-like CUINs is the component with the highest technical risk, exerting a significant impact on both network reliability and economic feasibility. This paper designs and constructs a minimal model and a basic model of a constant-current remote power supply system (CCRPSS) for grid-like CUINs. Through simulation modeling and analysis, the system’s capability to handle faults in a single underwater unit or multiple underwater units in different power supply link segments (PSLSs) is validated, and the impact of underwater unit faults on the system’s operational state is analyzed. Based on this, a descriptive method for determining the power supply reliability (PSR) of observation equipment (OE) is proposed, and the variation patterns of this reliability across different power supply links (PSLs) are derived. Building on this foundation, a constrained engineering design method for the grid-like CCRPSS is proposed. This method aims to deploy a larger number of secondary nodes (SNs) at a lower cost. By integrating constraints including the PSR of OE for each PSL, the open-circuit and short-circuit fault rates of underwater units, and the allowable number of SNs per PSLS, it optimizes the system engineering design problem. This approach yields an optimal solution for the number of longitudinally and transversely deployed SNs as well as the reliability requirements for each underwater unit. Case simulation results validate the descriptive method for the PSR of OE and the variation patterns of such reliability, thereby confirming the feasibility of the constrained engineering design approach. The research findings presented in this paper can provide theoretical references for the reliability analysis, scale design, and long-term planning of CUINs and their remote power supply systems. Full article

The Aedes aegypti mosquito exhibits a critical behavioral adaptation through its oviposition strategy, laying eggs in dry and wet environments just above the water level, allowing eggs to resist desiccation and hatch only when submerged by rain. To investigate this mechanism, we developed a nonlinear dynamic model incorporating climate-driven parameters affecting egg hatching and adult emergence. Theoretical analysis revealed an imperfect pitchfork bifurcation giving rise to a phenomenon we term basin-mediated hysteresis. Unlike classical hysteresis, which relies on coexisting stable states, this mechanism results from the progressive collapse of the extinction basin boundary. As the control parameter approaches its critical value, the basin of attraction of the trivial equilibrium shrinks. Once the population establishes itself above the threshold, returning the parameter below unity does not restore extinction, leading to an irreversible transition governing population persistence. The model was validated using field data from mosquito traps in a Brazilian city, showing strong agreement with observed seasonal patterns of female captures. Parameters were optimized using the Differential Evolution algorithm, yielding high correlation between model and field data. The results demonstrate that the dual oviposition strategy underlies population persistence and seasonal peaks, providing information for planning interventions amid global arbovirus expansion. Full article

Information propagation in social media has attracted the wide attention of scholars, with great progress made in empirical and modeling studies. Branching processes, extensively utilized in theoretical biology, are increasingly applied to model information diffusion dynamics. However, detailed and data-driven studies that implement this methodology remain rare. This study, utilizing empirical data, characterizes and models information diffusion in social media with branching processes. The reliability of the branching model is verified through the comparison of theoretical predictions, numerical simulations, and empirical results, and the model can replicate the key statistical characteristics observed in realistic cascades. The research results validate the applicability of branching processes in information diffusion, and contribute to the development of more elaborate, data-driven models of information spreading in complex real-world scenarios. Full article

Accurate and interpretable assessment of cognitive workload from EEG remains a central challenge in neuroergonomics and real-time human–machine interaction. To address the limitations of existing Empirical Mode Decomposition (EMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) approaches, particularly their instability, limited neuroscientific grounding, and sensitivity to amplitude fluctuations, this paper introduces Scale-Stabilized Relative Intrinsic Mode Energy (SS-RIME), a theoretically motivated and physiologically informed feature extraction framework. SS-RIME integrates instantaneous frequency stabilization to enforce a consistent oscillatory hierarchy across subjects, delta (1–4 Hz) and theta (4–7.5 Hz) spectral weighting based on established frontal-midline activity, and cross-IMF energy normalization to reduce amplitude-driven variability. Applied to 64-channel EEG recorded during N-back tasks, the proposed framework achieved high performance, outperforming both classical machine-learning baselines and deep learning models such as EEGNet, DeepConvNet, and ShallowConvNet. SS-RIME yielded accuracies of $99.12\pm 0.41\%$ (0 vs. 2-back), $97.84\pm 0.63\%$ (0 vs. 3-back), and $92.31\pm 1.12\%$ (2 vs. 3-back), demonstrating strong cross-subject generalization. Theta-dominant IMFs over frontal midline regions emerged as the most discriminative components, supporting the neuroscientific validity of the stabilized and spectrally weighted Hilbert–Huang representation. With an inference time below 20 ms per epoch, SS-RIME is computationally efficient and suitable for real-time neuroergonomics applications, providing a robust, explainable, and physiologically grounded solution for EEG-based cognitive workload decoding while addressing key methodological gaps in prior EMD/CEEMDAN and deep learning approaches. Full article

The digital preservation and dissemination of historical and cultural heritage is a pivotal area at the intersection of digital humanities and geographic information science. To address the challenges of multi-source heterogeneity, limited dimensionality, and inadequate public engagement, this study designed and implemented an interactive visualization platform using modern Web technologies. Taking the Leshan Confucian Temple (religious heritage) and the former site of Wuhan University (educational heritage) as case studies, the platform integrates four types of heterogeneous data (geospatial coordinates, architectural attributes, visitor behavioral records, and multimedia imagery) into a unified spatiotemporal information model. Core technical implementations are built upon a lightweight front-end stack including the Gaode Map JavaScript API for geographic visualization, ECharts for dynamic statistical charting, and the Tailwind CSS framework for a fully responsive front-end interface. Key interactive features encompass linked map markers with contextual information windows, user-driven chart filtering, and paginated loading of cultural relic cards. Evaluation results demonstrate that the platform achieves cross-device response delay ≤3 s, supports spatially grounded, dynamic, and presentation of cultural heritage information, and attains a System Usability Scale (SUS) score of 82.5. This work offers a lightweight, scalable technical solution for advancing digital recording and public communication of historical and cultural heritage, while contributing to the theoretical discourse on spatial narrative and multi-source data integration in digital humanities. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature–like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature–like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩~r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

Background: H1-antihistamines are widely used for allergic and upper respiratory conditions; however, several agents included in this class have been associated with cardiac electrophysiological adverse effects, including QT interval prolongation and torsades de pointes (TdP). These effects are largely exposure-dependent and mechanistically linked to inhibition of cardiac ion channels. Chlorpheniramine maleate (CPM), a first-generation H1-antihistamine, has been implicated in arrhythmic events primarily under conditions of increased systemic exposure, prompting interest in whether alternative routes of administration may lower cardiac risk. Methods: This narrative review integrates mechanistic, preclinical, clinical, pharmacokinetic, and regulatory evidence. Information was extracted from PubMed, Google Scholar, and Scielo using search terms such as cardiotoxicity, chlorpheniramine, QT prolongation, intranasal administration, and cardiac arrhythmias, with no language restriction. Results: Comparative pharmacokinetic evidence shows that, on a dose-normalized basis, intranasal and oral chlorpheniramine exhibit comparable bioavailability; however, in a clinical context, intranasal doses (1.12–2.24 mg) are lower than oral daily doses (4–12 mg/day), resulting in a lower systemic exposure (Cmax and AUC) with intranasal administration. Available pharmacovigilance or epidemiological data have not specifically evaluated intranasal chlorpheniramine, and the number of dedicated safety trials remains limited. Conclusions: Preclinical, in vitro, mechanistic studies suggest that intranasal administration of chlorpheniramine should confer superior cardiac safety compared to the oral route. However, clinical data from human studies directly comparing the cardiac safety of intranasal chlorpheniramine versus systemic chlorpheniramine is extremely limited. More data from clinical trials, case–control studies, and regulatory databases are needed to validate these theoretical claims. Full article

The intersection of digital citizenship, sustainable education, and community belonging represents an emerging yet underexplored area of inquiry, particularly within Arab higher education contexts where institutional digitalization is accelerating alongside distinct sociocultural expectations around academic identity. This study aims to investigate the mediating role of sustainable education in the relationship between digital citizenship and community belonging among Egyptian university students. A quantitative cross-sectional survey design was employed with a main sample of 819 university students. Participants completed three validated instruments: the Revised Digital Citizenship Scale, the Sustainable Education Scale, and the Where I Belong Survey. Mediation analysis was conducted using Hayes’ PROCESS macro with 5000 bootstrap resamples. Results reveal that digital citizenship is significantly and positively associated with both sustainable education and community belonging. Sustainable education, in turn, significantly predicts community belonging after controlling for digital citizenship, with the indirect effect accounting for approximately 38% of the total effect, consistent with partial mediation. These findings demonstrate that responsible digital engagement is associated with community belonging not only directly but also in a pattern statistically consistent with partial mediation through sustainability-oriented values including equity, inclusiveness, and democratic participation. These findings suggest theoretically informed directions for future intervention design, wherein integrating sustainable education principles into digital learning environments may warrant empirical investigation as a potential approach to cultivating ethically grounded, socially cohesive academic communities. Full article

This study aimed to develop and validate a physics-informed neural network (PINN) framework for data-efficient and physically consistent process optimization in the laser powder bed fusion (LPBF) of Inconel 718 (IN718) superalloy. Laser powder bed fusion (LPBF) is widely adopted for fabricating Inconel 718 (IN718) components in aerospace and energy applications; however, navigating its high-dimensional, nonlinear process parameter space remains a central challenge. High-fidelity finite element simulations are computationally prohibitive for extensive parameter sweeps, whereas purely data-driven machine learning (ML) models are limited by data scarcity and unphysical extrapolation behavior. This study presents a physics-informed neural network (PINN) framework that embeds the transient heat conduction equation and Goldak double-ellipsoidal heat source model directly into the neural network training loss, enforcing thermophysical consistency simultaneously with data fidelity. The model was trained on a curated, multi-source dataset of LPBF IN718 parameter combinations drawn from peer-reviewed experimental studies and validated finite element simulation outputs, spanning the laser power (70–400 W), scan speed (200–2000 mm/s), hatch spacing (50–140 µm), and layer thickness (20–50 µm). The PINN predicted the melt pool width, depth, peak temperature, and relative density with mean absolute percentage errors (MAPE) of 3.8%, 4.7%, 3.1%, and 1.9%, respectively, outperforming a baseline artificial neural network (ANN) with an identical architecture. The framework correctly identified the optimal volumetric energy density (VED) window of 55–105 J/mm³, yielding relative densities ≥99.5%, consistent with the published experimental thresholds for IN718. A data efficiency analysis demonstrated that the PINN with 25% training data achieves a performance equivalent to that of the fully trained ANN with 100% data, confirming an approximately four-fold data efficiency improvement attributable to physics-informed regularization, consistent with theoretical predictions. Sensitivity analysis via automatic differentiation confirmed that laser power and scan speed were the dominant parameters (~85% combined variance), which is in agreement with previous studies. This study provides a computationally efficient, interpretable, and physically consistent ML pathway for the accelerated process qualification of IN718 components for aerospace and energy applications. Full article

Contemporary enterprise IT operations are largely implemented on Shannon–Turing computing models in which programs execute read–compute–write cycles over data structures, while governance—fault handling, configuration control, auditability, continuity, and accounting—is applied externally through infrastructure platforms, observability stacks, and human operational processes. This separation scales analytical throughput but accumulates what we term coherence debt: locally expedient operational commitments whose provenance and revisability degrade over time until exposed by failures, security incidents, regulatory demands, or architectural transitions. This paper examines the evolution of operational computing models that integrate com-pupation with regulation at two distinct levels. First, Distributed Intelligent Managed Elements (DIME) extend the classical Turing cycle toward a supervised execution loop—read–check-with-oracle–compute–write—by incorporating signaling overlays and FCAPS (Fault, Configuration, Accounting, Performance, and Security) supervision into computation in progress. Second, the Autopoietic Management and Orchestration System (AMOS), grounded in the General Theory of Information, the Burgin–Mikkilineni Thesis, and Deutsch’s epistemic framework, fully decouples process executors from governance by treating any Turing-equivalent engine as a replaceable execution substrate while elevating knowledge structures—encoded as local and global Digital Genomes—to first-class operational state within a governed knowledge network. Using a distributed microservice transaction testbed, we demonstrate how this approach operationalizes topology-as-data, a capability-oriented control plane, decoupled application-layer FCAPS independent of infrastructure management, and policy-selectable consistency/availability semantics. Our results show that the principal benefit of AMOS is not circumventing theoretical constraints such as the Consistency, Availability, and Partition tolerance (CAP) theorem, but governing their trade-offs as explicit, auditable commitments with defined convergence pathways and controlled return to a coherent system state, thereby reducing coherence debt and improving operational reliability in distributed AI-enabled enterprise systems. Full article

Joint modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring constitutes one of the most critical functions integrated in digital coherent receivers, ensuring high flexibility and stability in elastic optical networks (EONs). Since signal amplitude information captures inherent characteristics associated with modulation formats and fluctuations induced by OSNR variations, a simple and effective optical performance monitoring (OPM) scheme based on an amplitude-analytic complex plane is proposed. By employing a multi-task learning algorithm incorporating the multi-order gated aggregation (MOGA) module, the proposed scheme enables simultaneous MFI and OSNR monitoring for polarization division multiplexed (PDM)-QPSK/-16QAM/-32QAM/-64QAM/-128QAM signals. The performance of the proposed scheme is numerically verified in 28 GBaud coherent optical communication systems of various configurations. Numerical simulation results show that 100% identification accuracy is obtainable for all five modulation formats, even at OSNR values lower than the corresponding theoretical 20% forward error correction (FEC) limit. Meanwhile, the mean absolute error (MAE) of OSNR monitoring for QPSK, 16QAM, 32QAM, 64QAM, and 128QAM are 0.16 dB, 0.15 dB, 0.17 dB, 0.28 dB, and 0.33 dB, respectively. Furthermore, simulation results show that the proposed scheme is robust to residual chromatic dispersion (CD) and the nonlinear effects with strong generalization capability. These results suggest that the proposed scheme is promising for applications in next-generation EONs. Full article