Search Results (670)

This study focuses on the evolution of the Internet Water Army on social media, identifying a novel form known as artificial-intelligence-generated-content-enhanced social bots (AESBs), and compares their structural influence with traditional social bots in the context of public opinion guidance. Based on 3 years of real-world data from Weibo, this study develops a comprehensive framework integrating bot account detection, AESB content identification, and quantitative assessments of opinion guidance. A large-scale opinion propagation network is constructed to examine the structural roles of traditional social bots and AESB across three analytical levels: the node, community, and overall network. The results reveal substantial differences between AESB and traditional social bots. Social bots play a limited guiding role but help maintain network connectivity. In contrast, AESBs produce highly consistent and human-like content that demonstrates a significant capacity to reinforce topic focus, amplify emotional homogeneity, and deepen diffusion pathways, indicating a shift toward strategic content manipulation. These results suggest that AESBs are not merely passive generators but active agents of structural opinion control, capable of combining human mimicry with machine-level efficiency. This study advances theoretical understanding of IWA manipulation mechanisms, provides a replicable methodological approach, and offers practical implications for platform governance. Full article

Entropy-based analyses have emerged as a powerful tool for quantifying the complexity, regularity, and information content of complex biological signals, such as electroencephalography (EEG). In this regard, EEG-based lie detection offers the advantage of directly providing more objective and less susceptible-to-manipulation results compared to traditional polygraph methods. To this end, this study proposes a novel multi-scale entropy approach by fusing fuzzy entropy (FE), time-shifted multi-scale fuzzy entropy (TSMFE), and hierarchical multi-band fuzzy entropy (HMFE), which enables the multidimensional characterization of EEG signals. Subsequently, using machine learning classifiers, the fused feature vector is applied to lie detection, with a focus on channel selection to investigate distinguished neural signatures across brain regions. Experiments utilize a publicly benchmarked LieWaves dataset, and two parts are performed. One is a subject-dependent experiment to identify representative channels for lie detection. Another is a cross-subject experiment to assess the generalizability of the proposed approach. In the subject-dependent experiment, linear discriminant analysis (LDA) achieves impressive accuracies of 82.74% under leave-one-out cross-validation (LOOCV) and 82.00% under 10-fold cross-validation. The cross-subject experiment yields an accuracy of 64.07% using a radial basis function (RBF) kernel support vector machine (SVM) under leave-one-subject-out cross-validation (LOSOCV). Furthermore, regarding the channel selection results, PZ (parietal midline) and T7 (left temporal) are considered the representative channels for lie detection, as they exhibit the most prominent occurrences among subjects. These findings demonstrate that the PZ and T7 play vital roles in the cognitive processes associated with lying, offering a solution for designing portable EEG-based lie detection devices with fewer channels, which also provides insights into neural dynamics by analyzing variations in multi-scale entropy. Full article

Global Navigation Satellite System (GNSS) spoofing, which manipulates PVT solutions through false measurements, increasingly threatens GNSS reliability and user safety. However, most existing simulator-based spoofers, constrained by their inability to access real-time ionospheric information (e.g., Global Ionosphere Maps, GIMs) from external sources, struggle to replicate authentic total electron content (TEC) along each signal propagation path accurately and in a timely manner. In contrast, widespread dual-frequency (DF) receivers with access to the internet can validate local TEC measurements against external references, establishing a pivotal spoofing detection distinction. Here, we propose an Ionospheric Information-Assisted Spoofing Detection Technique (IIA-SDT), exploiting the inherent consistency between TEC values derived from DF pseudo-range measurements and external references in spoofing-free scenarios. Spoofing probably disrupts this consistency: in simulator-based full-channel spoofing where all channels are spoofed, the inaccuracies of the offline ionospheric model used by the spoofer inevitably cause TEC mismatches; in partial-channel spoofing where the spoofer fails to control all channels, an unintended PVT deviation is induced, which also causes TEC deviations due to the spatial variation of the ionosphere. Basic principles and theoretical analysis of the proposed IIA-SDT are elaborated in the paper. Simulations using ionospheric data collected from 2023 to 2024 at a typical mid-latitude location are conducted to evaluate IIA-SDT performance under various parameter configurations. With a window length of 5 s and satellite number of 8, the annual average detection probability approximates 75% at a false alarm rate of $1\times {10}^{-3}$, with observable temporal variations. Field experiments across multiple scenarios further validate the spoofing detection capability of the proposed method. Full article

Multimedia Forensics (MMF) investigates techniques to automatically assess the integrity of multimedia content, e.g., images, videos, or audio clips. Data-driven methodologies like Neural Networks (NNs) represent the state of the art in the field. Despite their efficacy, NNs are often considered “black boxes” due to their lack of transparency, which limits their usage in critical applications. In this work, we assess the interpretability properties of Deep High-Frequency Residuals (DHFRs), i.e., noise residuals extracted from images by NNs for forensic purposes, that nowadays represent a powerful tool for image splicing localization. Our research demonstrates that DHFRs not only serve as a visual aid in identifying manipulated regions in the image but also reveal the nature of the editing techniques applied to tamper with the sample under analysis. Through extensive experimentation on spliced amplitude Synthetic Aperture Radar (SAR) images, we establish a correlation between the appearance of the DHFRs in the tampered-with zones and their high-frequency energy content. Our findings suggest that, despite the deep learning nature of DHFRs, they possess significant interpretability properties, encouraging further exploration in other forensic applications. Full article

Zinc (Zn) deficiency affects over 30% of the global population, with the highest burdens in developing countries reliant on cereal-based diets. As a major dietary staple in regions such as Sub-Saharan Africa and Latin America, common bean (Phaseolus vulgaris L.) represents a promising vehicle for addressing hidden hunger. This review critically evaluates the efficacy of various strategies to enhance Zn concentration in common bean, ranging from agronomic to genetic manipulation, and proposes promising strategies for biofortifying common bean in developing countries that are resource- and technology-limited. Biofortification strategies include agronomic practices, conventional breeding, and genetic engineering, each with distinct strengths and limitations. Agronomic methods such as soil and foliar fertilization can rapidly increase micronutrient content, but they require recurrent costs and may not be sustainable for smallholders without subsidies. Genetic engineering, particularly transgenic approaches, can significantly boost Zn levels; however, regulatory hurdles, cost of production, and public acceptance remain significant obstacles to widespread adoption. Conventional breeding is secure and widely adopted, but is time-consuming and limited by genetic diversity, making it less precise and slower than genetic engineering. We argue for a context-specific and integrated biofortification framework that prioritizes agronomic interventions such as biofertilizer, seed priming, soil Zn application, and foliar Zn application as approaches for quick results. Moderate- to long-term progress towards a biofortified common bean can be achieved using conventional breeding methods by selecting for local germplasm that accumulates higher Zn amounts in grain. On the other hand, genetic engineering is best for rapid, targeted nutrient enhancement where genetic diversity is lacking, but faces regulatory and acceptance challenges. We recommend that policymakers prioritize frameworks that harmonize these approaches, improve communication and education regarding the benefits of biofortified crop produce, subsidize and strengthen biofortified seed systems, and promote soil health initiatives. Full article

This paper proposes a novel perceptual image hashing scheme that robustly combines global structural features with local texture information for image authentication. The method starts with image normalization and Gaussian filtering to ensure scale invariance and suppress noise. A saliency map is then generated from a color vector angle matrix using a frequency-tuned model to identify perceptually significant regions. Local Binary Pattern (LBP) features are extracted from this map to represent fine-grained textures, while rotation-invariant Zernike moments are computed to capture global geometric structures. These local and global features are quantized and concatenated into a compact binary hash. Extensive experiments on standard databases show that the proposed method outperforms state-of-the-art algorithms in both robustness against content-preserving manipulations and discriminability across different images. Quantitative evaluations based on ROC curves and AUC values confirm its superior robustness–uniqueness trade-off, demonstrating the effectiveness of the saliency-guided fusion of Zernike moments and LBP for reliable image hashing. Full article

Porcine adenovirus serotype 5 (PAdV-5) is an emerging viral vector platform for veterinary vaccines; however, its genomic plasticity and essential replication elements remain incompletely characterized. This study reports the isolation and reverse genetic manipulation of a novel PAdV-5 strain (GD84) from diarrheic piglets in China. PCR screening of 167 clinical samples revealed a PAdV-5 detection rate of 38.3% (64/167), with successful isolation on ST cells after three blind passages. The complete GD84 genome is 32,620 bp in length and exhibited 99.0% nucleotide identity to the contemporary strain Ino5, but only 97.0% to the prototype HNF-70. It features an atypical GC content of 51.0% and divergent structural genes—most notably the hexon gene (89% identity to HNF-70)—suggesting altered immunogenicity. Using Red/ET recombineering, we established a rapid (less than 3 weeks) reverse genetics platform and generated four E3-modified recombinants: ΔE3-All-eGFP, ΔE3-12.5K-eGFP, ΔE3-12.5K+ORF4-eGFP, and E3-Insert-eGFP. Crucially, the ΔE3-All-eGFP construct (complete E3 deletion) failed to be rescued, while constructs preserving the 12.5K open reading frame (ORF) yielded replication-competent viruses with sustained eGFP expression over three serial passages and titers over 10^7.0 TCID₅₀/mL. Fluorescence intensity was inversely correlated with genome size, as the full-length E3-Insert-eGFP virus showed reduced expression compared with the ΔE3 variants. Our work identifies the 12.5K ORF as essential for PAdV-5 replication and provides an optimized vaccine engineering platform that balances genomic payload capacity with replicative fitness. Full article

High-order linear polarization (LP) modes and vortex beams carrying orbital angular momentum (OAM) are highly useful in various fields. High-order LP modes provide higher thresholds for nonlinear effects, reduced sensitivity to distortions, and better energy extraction in high-power lasers. OAM beams are useful in optical communication, imaging, particle manipulation, and fiber sensing. The ability to switch between these mode outputs enhances system versatility and adaptability, supporting advanced applications both in research and industry. This paper presents the design of a 19 × 1 photonic lantern capable of outputting 19 LP modes and 16 OAM modes with low loss. Using the beam propagation method, we simulated and analyzed the mode evolution process and insertion loss, and we calculated the transmission matrix of the photonic lantern. The results indicate that the designed device can efficiently evolve into these modes with a maximum insertion loss not exceeding 0.07 dB. Furthermore, an adaptive control system was developed by introducing a mode decomposition system at the output and combining it with the Stochastic Parallel Gradient Descent (SPGD) + basin hopping algorithm. Simulation results show that this system can produce desired modes with over 90% mode content, demonstrating promising application prospects in switchable high-order mode systems. Full article

This study investigates how algorithm-driven content curation impacts mediated discourse, amplifies ideological echo chambers and alters linguistic structures in online communication. While these platforms promise connectivity, their engagement-driven mechanisms reinforce biases and fragment discourse spaces, leading to Synthetic Social Alienation (SSA). By combining discourse analysis with in-depth interviews, this study examines the algorithmic mediation of language and meaning in digital spaces, revealing how algorithms commodify attention and shape conversational patterns. In this study, four SSA patterns were identified: Algorithmic Manipulation, Digital Alienation, Platform Dependency, and Echo Chamber Effects. A hybrid dataset (180 training, 30 test samples) was used to train classification models. Among four algorithms, Support Vector Machine (SVM) achieved the highest performance (90.0% accuracy, 90.4% F1-score). Sentiment analysis revealed distinct language structures for positive (AUC = 0.994), neutral (AUC = 0.933), and negative (AUC = 0.919) expressions. SHAP and LIME analyses highlighted key features driving model decisions. The findings expose how digital platforms commodify attention and shape user discourse, underscoring the need for ethical algorithm design and regulatory oversight. Full article

Background: Cognitive biases in information processing, particularly attentional and memory biases, play a crucial role in the development and maintenance of Major Depressive Disorder (MDD). These biases lead individuals with MDD to preferentially attend to and remember negative information, thereby maintaining a depressed mood. A recently proposed attentional resources model suggests that exposure to negative stimuli leads to deeper cognitive processing of subsequent information, regardless of its content. Based on this model, the current study investigated a novel paradigm that manipulated exposure to negative emotional stimuli and examined its effect on information processing and mood improvement. Method: Thirty-eight unmedicated participants with MDD and no comorbid disorders, and 37 healthy controls, completed three blocks of an emotional recall task, which involved watching a short emotional video followed by a recall task of neutral or positive valence stories. Mood changes were assessed throughout the task. Results: Results revealed that both the MDD and HC groups reported improved mood after exposure to a negative emotional video followed by a positive story. Conclusions: These results have important clinical implications. The paradigm may be applied in a broader sense as an active tool that may help to improve mood in depression treatment. Full article

Representational Theories of Mind have long dominated Cognitive Translation Studies, typically assuming that translation involves the manipulation of internal representations (symbols) that stand in for external states of affairs. In recent years, classical representationalism has given way to more nuanced, inferential, interpretive, context-sensitive, and modern representational models, some of which align naturally with probabilistic and predictive approaches. While these frameworks remain broadly compatible with one another, radical enactivism offers a more disruptive alternative: it denies representational content altogether, viewing translation instead as an affectively grounded, context-sensitive, self-evidencing activity shaped by the translator’s embodied engagement with text, context, and sociocultural norms. From an enactivist standpoint, translation emerges not from static symbolic mappings, but from situated, embodied, and affectively modulated inference processes that dynamically negotiate meaning across languages. The paper provides a theoretical synthesis, arguing that the Free Energy Principle under Predictive Processing and Active Inference provides a suitable mathematical framework amenable to representational and enactive accounts. Full article

As environmental conservation and community development gain importance, ecotourism has emerged as a significant segment of the global tourism industry. However, the cultural factors that drive tourist behavior in this domain remain underexplored. This research examined how power distance belief (PDB), interacts with the type of tourism content shared on social media (conspicuous versus experiential) to influence travelers’ ecotourism intentions. To test our hypotheses, we conducted two experimental studies using a 2 (PDB: high vs. low) × 2 (tourism content type: conspicuous vs. experiential) between-subjects design. Participants for both experiments (N = 480) were recruited through an online survey platform. In the experiments, participants’ PDB was situationally primed, and tourism content type was manipulated using specifically created fictitious posts adapted from a real social media platform. Other key variables were measured using validated multi-item scales. Data were analyzed using analysis of variance (ANOVA) and moderated mediation analysis (PROCESS Model 15). The findings reveal that travelers with high PDB show higher ecotourism intentions when exposed to conspicuous content, whereas travelers with low PDB exhibit higher intentions when exposed to experiential content. This interactive effect is mediated by travelers’ social comparison motives. These findings offer novel insights into the motivations underlying ecotourism behavior by identifying distinct pathways through which social media can promote sustainable tourism behaviors, and provide practical guidance for eco-destination managers to design targeted marketing strategies that encourage sustainable tourism practices across different consumer segments. Full article

Traditional culture plays a vital role in shaping national identity and emotional belonging, making it imperative to explore innovative strategies for its digital preservation and engagement. This study investigates how interaction design in cultural digital games influences users’ emotional experiences and cultural understanding. Centering on the Chinese intangible cultural heritage puppet manipulation, we developed an interactive cultural game with three modes: gesture-based interaction via Leap Motion, keyboard control, and passive video viewing. A multimodal evaluation framework was employed, integrating subjective questionnaires with physiological indicators, including Functional Near-Infrared Spectroscopy (fNIRS), infrared thermography (IRT), and electrodermal activity (EDA), to assess users’ emotional responses, immersion, and perception of cultural content. Results demonstrated that gesture-based interaction, which aligns closely with the embodied cultural behavior of puppet manipulation, significantly enhanced users’ emotional engagement and cultural comprehension compared to the other two modes. Moreover, fNIRS data revealed broader activation in brain regions associated with emotion regulation and cognitive control during gesture interaction. These findings underscore the importance of culturally congruent interaction design in enhancing user experience and emotional resonance in digital cultural applications. This study provides empirical evidence supporting the integration of cultural context into interaction strategies, offering valuable insights for the development of emotionally immersive systems for intangible cultural heritage preservation. Full article

This study investigates the impact of environmental changes induced by systematic manipulation of flooding depth and breeding density on greenhouse gas emissions in the field-based giant rice–fish hybrid farming model. Compared with traditional agricultural practices, increasing cultured density in giant rice–fish co-cultivation significantly alleviated the adverse consequences of flooding on soil nutrient dynamics, microbial activity community structure, and greenhouse gas emissions. Relative to the traditional alternating wet and dry irrigation, the soil concentrations of ammonium, total nitrogen, and phosphate significantly increased. Cultured fish had significantly increased soil microbial biomass carbon, nitrogen, and phosphorus contents and improved soil β-glucosidase and aryl-sulfatase activates relative to flooding alone. Cultured fish increased the relative abundances of Actinobacteria, Nitrospirae, Planctomycetes, Verrucomicrobia, and Aminicenantes. An increasing cultured fish density reduced cumulative methane and nitrous oxide emissions and GWP (global warming potential). Relative to the continuous flooding throughout the growing period, cumulative methane emissions and GWP in the flooding with high-density cultured fish were reduced by 5.32% and 1.48%, respectively. Notably, this co-cultivation strategy has the potential to transform traditional practices for sustainable agriculture. Nevertheless, it is imperative to remain vigilant about the potential consequences of greenhouse gas emissions associated with these innovative practices. Continuous monitoring and refinement are essential to ensure the long-term sustainability and viability of this agricultural approach. Full article

As information technology advances, digital content has become widely adopted across diverse fields such as news broadcasting, entertainment, commerce, and forensic investigation. However, the availability of sophisticated multimedia editing tools has significantly increased the risk of video and image forgery, raising serious concerns about content authenticity at both societal and individual levels. To address the growing need for robust and accurate detection methods, this study proposes a novel video forgery detection model that integrates both spatial and frequency-domain features. The model is built on a ResNet-LSTM framework enhanced by a Convolutional Block Attention Module (CBAM) for spatial feature extraction, and further incorporates Discrete Cosine Transform (DCT) to capture frequency domain information. Comprehensive experiments were conducted on several mainstream benchmark datasets, encompassing a wide range of forgery scenarios. The results demonstrate that the proposed model achieves superior performance in distinguishing between authentic and manipulated videos. Additional ablation and comparative studies confirm the contribution of each component in the architecture, offering deeper insight into the model’s capacity. Overall, the findings support the proposed approach as a promising solution for enhancing the reliability of video authenticity analysis under complex conditions. Full article