Search Results (472)

This article examines the utilization of WhatsApp as an alternative communication tool for disseminating visual content among social activists during protests. While WhatsApp is typically conceptualized as an interpersonal or group messaging platform, research on its role as an infrastructure for alternative media and citizen journalism remains limited. The study focuses on the “Tikva” group, established at the onset of the public struggle against the 2023 judicial reform in Israel, which evolved into a nine-month mass protest movement described as one of the largest in the country’s history. Through qualitative thematic content analysis of videos distributed within the group, the article explores how WhatsApp functions simultaneously as a channel for digital activism and as a site of bottom-up, democratic, non-institutional news production. The findings indicate two primary trends: functionally, WhatsApp operates as a mechanism for resource mobilization, calls to action in physical and digital spaces, and the cultivation of belonging and solidarity among activists facing institutional power; in terms of content and production, the videos articulate an anti-hegemonic discourse and challenge mainstream media conventions. The analysis shows how these videos dismantle delegitimizing frames and construct a counter-narrative depicting protesters as citizens defending democracy, thereby sustaining the protest movement’s momentum. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

This study examines whether retail social media sentiment and community attention explain daily net capital flows into U.S. spot Bitcoin exchange-traded funds (ETFs), and whether issuer brand visibility conditions that relationship. We construct a balanced panel of $N=10$ ETFs over $T=514$ trading days (January 2024 to January 2026) and combine it with 162,819 cleaned Reddit posts to derive three AI-driven discourse variables: engagement-weighted sentiment, community attention, and a novel issuer-specific BrandScore. Entity fixed-effects regressions show that neither aggregate sentiment nor BrandScore level alone significantly predicts fund-level flows; however, the Sentiment $\times$ BrandScore interaction is significant ($\widehat{\beta }=2.930$, $p=0.038$), indicating that sentiment becomes economically meaningful only when attached to a visible issuer. This interaction survives two-way (entity + date) fixed effects ($p=0.012$) and winsorization ($p=0.004$). Panel quantile regressions reveal distributional heterogeneity in the brand-sentiment channel. Rolling 90-day window estimation confirms the mechanism is episodic, with the interaction achieving significance in 62.8% of subsample windows. These results provide suggestive evidence for a brand-filtered sentiment transmission mechanism in digital asset markets. Full article

As wireless networks evolve from 5G toward 6G, the complexity of the electromagnetic environment increases sharply. Spectrum usage expands significantly into millimetre-wave (mmWave) and terahertz (THz) high-frequency bands. Network node density and mobility increase markedly. Moreover, communication-sensing-computation functions are deeply integrated. Accurate, real-time, full-band Electromagnetic Spectrum Maps (ESMs) have become a core infrastructure for 6G spectrum situational awareness, Dynamic Spectrum Access (DSA), interference coordination, and Integrated Sensing and Communication (ISAC). However, while a growing body of recent work extends radio mapping to multi-band and temporal domains, the predominant focus of existing Radio Map research remains the two-dimensional spatial power distribution at a single fixed frequency—essentially a degenerate special case of ESM after the frequency and time dimensions are collapsed—and no existing survey unifies 3D spatial construction, time-varying prediction, and full 6G system integration under a shared 4D formalism. This paper focuses on the three core research dimensions of ESMs, i.e., 3D spatial ESM construction, dynamic time-varying ESM modelling and prediction, and ESM integration with 6G systems. Under a unified four-dimensional ESM framework (space × frequency × time × power), we clarify the hierarchical relationships among ESM/SEM/REM/Radio Map/Channel Knowledge Maps (CKMs). Then, we systematically review 3D ESM construction, dynamic ESM modelling and prediction, and the integration of ESM with CKM/Digital Twin Networks (DTNs)/ISAC. Finally, we identify five, core open problems that constrain the development of the field to provide a systematic reference for 6G intelligent spectrum management research. Full article

This study examines the circulation of disinformation during the 2024 València DANA (high-altitude isolated depression), which produced torrential rainfall across eastern and southern Spain between 29 and 31 October 2024. Using a quantitative content analysis, it analyzes the 100 most viral false or misleading claims, classifying them by typology, format, dissemination channel, and narrative strategy. Findings show an ecosystem dominated by conspiracy narratives about the causes of the disaster and by audiovisual content—particularly short videos and images—which achieved substantially greater reach than textual posts. Narrative mechanisms such as decontextualization, emotional appeal, and political polarization were recurrent and often combined. Verification efforts that matched the original format were associated with higher relative correction reach, although their observable diffusion remained lower than that of the false claims in the analyzed sample. Overall, the study highlights the cross-platform and multimodal dynamics of crisis disinformation and underscores the need for proactive, technologically supported communication strategies. These include automated monitoring, multimodal verification, and interoperable digital infrastructures for crisis communication. Full article

Evaporation ducts are the dominant natural medium for maritime beyond-line-of-sight communications. However, their minute-scale temporal variability causes significant decision latency and resource underutilization in conventional passive communication systems. This paper proposes an active communication framework driven by an ocean meteorological large model. A digital twin of the evaporation duct is constructed using a physics-informed neural network, fused with multi-source marine observations and real-time link feedback. Spatiotemporal Fourier neural operators accelerate electromagnetic propagation calculations to millisecond latency, and temporal generative models realize probabilistic channel prediction with rigorous uncertainty quantification. A distributed robust model predictive control scheme is designed to optimize communication resource allocation under confidence interval constraints. South China Sea simulations show that the proposed PINN digital twin achieves fitting MAEs of 0.13 m for duct height and 0.10 dB for path loss. Within a 10 min forecast horizon, the path loss prediction error remains below 0.98 dB. Compared with traditional passive methods, the proposed system reduces the safe outage probability by 46.3%, enhances effective throughput by 28.7%, and attenuates rainfall-induced band handover oscillation by 90.2%, enabling a methodological shift from reactive adaptation to proactive anticipation. Full article

This study investigates how parasocial interactions with travel influencers shape consumer–brand identification and purchase intention within hospitality-related virtual communities. Grounded in social influence theory and the literature on parasocial interaction, the study examines the mediating roles of perceived information quality and a sense of belonging in influencer-led digital environments. Data were collected from 940 consumers and analyzed using confirmatory factor analysis and structural equation modeling. The findings demonstrate that parasocial interaction significantly enhances perceived information quality and consumers’ sense of belonging to influencer-led virtual communities, thereby strengthening consumer–brand identification and purchase intention. The results further suggest that influencer-led social environments function not only as promotional channels but also as psychologically meaningful communities that shape consumer perceptions and behavioral outcomes. By integrating parasocial interaction, virtual community belonging, and consumer–brand identification within a hospitality context, this study contributes to the growing literature on influencer marketing and digital consumer behavior. Full article

Rapid localisation of trapped victims after urban disasters is essential but challenging because Bluetooth Low Energy (BLE) beacons are intermittent, radio propagation is obstructed by rubble, UAVs are energy-constrained, and real-world multi-UAV training is impractical in high-risk search-and-rescue (SAR) environments. This study formulates post-disaster victim localisation as a cooperative Dec-POMDP and adapts a model-aided federated multi-agent reinforcement learning framework based on FedQMIX. The proposed pipeline combines a lightweight LoS/NLoS surrogate channel model, PSO-based victim-position estimation, return-to-base and map-feasibility safety checks, an SAR-aligned shaped reward, and a leakage-free centralised training state based on estimated rather than ground-truth victim locations. Each UAV trains locally inside a learned digital-twin simulator and periodically shares only QMIX network parameters, avoiding the exchange of raw trajectories or RSSI logs. The framework is evaluated on two synthetic post-earthquake urban maps representing a compact return-to-base scenario and a larger reach-to-destination scenario. Across five independent seeds per method and map, Model-Aided FedQMIX achieves the highest and most stable victim-localisation performance, with the clearest advantage observed in the larger long-horizon scenario. Additional diagnostic tests examine reward-weight sensitivity, RF channel-shift robustness, BLE/smartphone hardware heterogeneity, non-IID client-data variation, and partial-client FedAvg under missing client updates. The results indicate that combining model-aided localisation cues, decentralised value factorisation, SAR-aligned objective design, and federated parameter sharing can improve the robustness of UAV-based victim-localisation policies. The framework also clarifies deployment considerations for federated SAR coordination, including communication payload, privacy boundaries, heterogeneous client experience, device variability, and intermittent connectivity. This study remains simulation-based, and future validation with real UAVs, BLE devices, and rubble-inspired testbeds is required before operational deployment. Full article

Start-ups have established themselves as drivers of dynamism and economic growth. However, they face many and varied challenges, with one of them being managing their communication strategy. This study aims to analyse the digital communication strategy of start-ups in the Agrotech and Foodtech sectors. For that purpose, the annual rankings published by El Referente between 2023 and 2025 were reviewed, and 17 companies were selected, 11 from the Agrotech sector and six from the Foodtech sector. Based on this sample, a mixed methodology was used, combining content analysis with social media metrics analysis. The results show an uneven use of communication strategies by start-ups. Almost all of them have their own communication channels, such as websites or social media profiles, but their use is not uniform, and in many cases, the potential offered by these tools could be improved. The findings contribute to the literature on strategic communication in start-ups by evidencing the gap between declared identity and substantive legitimacy in sectors of high social scrutiny, such as the agri-food sector. Full article

Edge Internet of Things (IoT) blockchain deployments increasingly rely on continuous transaction ingestion from resource-constrained IoT devices to nearby edge gateways over heterogeneous wireless links. In this setting, transient channel noise and packet corruption can inject invalid payloads into the edge processing pipeline and trigger unnecessary buffering, parsing, and, most critically, computationally expensive cryptographic operations such as digital signature verification. This leads to wasted computation, increased latency, and reduced energy efficiency at the edge, particularly under dense IoT traffic. This paper presents an energy-aware and fault-tolerant Quantum-Dot Cellular Automata (QCA)-based integrity pre-filter for IoT-to-edge blockchain transaction ingestion. At the circuit level, we adapt and modify a previously reported fault-tolerant five-input majority gate (MV5) structure and use it as a robust primitive for nanoscale integrity-screening circuits. Building on this modified MV5, we design a set of QCA integrity blocks, including a parity checker, a compact XNOR gate circuit, a parity-bit generation circuit, and a sender-to-channel/receiver nano-communication integrity workflow suitable for early screening of corrupted payloads. Compared with the best previously reported baseline considered in this study, the modified MV5 achieves 76.47% tolerance to single-cell omission defects, corresponding to a 17.47 percentage-point increase and an approximately 29.61% relative improvement over the prior 59% omission-tolerance result, while preserving 100% tolerance against extra-cell deposition defects. At the system level, the proposed circuit is discussed as a potential early screening stage for edge-IoT blockchain transaction ingestion. A bounded analytical model is used to estimate the possible reduction in unnecessary signature-verification workload under assumed corruption and detection conditions. This analysis is not intended as a deployment-level validation; full edge-node implementation, throughput measurement, queueing-delay evaluation, real traffic traces, retransmission behavior, and empirical signature-verification profiling remain future work. The proposed parity/chunk-parity pre-filter is designed for low-cost detection of random transmission-induced corruption and does not replace cryptographic authentication, hashing, digital signatures, CRC-based detection, or blockchain validation. All proposed designs are validated using QCADesigner tools. Full article

Very High Frequency (VHF) radio communication systems face significant challenges in modern electromagnetic environments, including spectrum congestion, dynamic interference, and varying channel conditions. Existing adaptive approaches rely on static rule-based switching or single-cycle optimization, which cannot accumulate operational experience across decision cycles. This paper proposes a digital twin-enabled online learning framework (DT-MAB) for adaptive waveform selection in tactical VHF communication. The framework employs a contextual multi-armed bandit algorithm (Lin-UCB) that continuously learns the mapping from channel conditions to optimal configurations, with the digital twin serving as a virtual exploration sandbox that screens candidate configurations before physical deployment—preventing link disruptions during exploratory actions. An expanded configuration space of 63 candidates (7 waveforms × 3 MAC protocols × 3 power levels) is constructed, and a hierarchical performance evaluation model combining voice quality, bit error rate, communication delay, and transmission range is developed using the Analytic Hierarchy Process (AHP) as the reward function for online learning. Experimental results across 10 random seeds demonstrate that DT-MAB achieves the lowest mean cumulative regret, reducing regret by 29% relative to MAB without a digital twin and by 16.5% relative to PSO-based optimization on average. Ablation experiments confirm that removing virtual exploration increases performance drop events by 49% (from 250 ± 79 to 373 ± 6), demonstrating that the digital twin is a functionally indispensable component of the online learning architecture. Full article

The growing emphasis on sustainable tourism has increased the importance of environmentally responsible practices in the hospitality industry. Green hotels have emerged as an important strategy for reducing environmental impacts while meeting the expectations of environmentally conscious travelers. However, limited research has examined the psychological mechanisms influencing international tourists’ decisions to choose green hotels in emerging tourism destinations. This study investigates the factors influencing international tourists’ motivation and intention to select green hotels in Chiang Mai Province, Thailand. A quantitative research design was employed using a structured questionnaire survey of 350 international tourists. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among sustainability image, perceived Environmental, Social, and Governance (ESG), environmental awareness, perceived value for money, trust in green hotels, motivation to choose green hotels, and Green Hotel Selection Intention. The results indicate that sustainability image, perceived ESG, and trust in green hotels significantly influence tourists’ motivation to choose green hotels, with trust demonstrating the strongest effect. In contrast, environmental awareness and perceived value for money do not show significant effects on motivation. Furthermore, motivation to choose green hotels has a strong positive influence on Green Hotel Selection Intention. These findings highlight the critical role of motivation as a psychological mechanism linking sustainability perceptions to tourists’ accommodation choices. The study contributes to the literature on sustainable tourism and green consumer behavior by integrating sustainability image and ESG perceptions into a comprehensive framework explaining Green Hotel Selection Intention. From a practical perspective, the findings suggest that hotel managers should strengthen trust through transparent sustainability communication, obtain recognized green certifications, and promote ESG initiatives through digital marketing channels. Destination policymakers in Chiang Mai may also support standardized green hotel accreditation programs to enhance tourists’ confidence in sustainable accommodation choices. These strategies can support the development of sustainable hospitality practices and enhance the competitiveness of green hotels in Chiang Mai and other tourism destinations. Full article

The integration of post-quantum digital signature (PQDS) algorithms into coherent wavelength-division multiplexing (WDM) optical networks introduces a non-negligible cryptographic overhead that fundamentally alters physical-layer performance characteristics. Unlike conventional studies that treat security and transmission independently, this work provides a cross-layer evaluation of PQDS-induced payload expansion and its direct impact on coherent optical system behavior under realistic, DSP-aligned conditions. A structured and reproducible evaluation framework is proposed to systematically analyze this interaction across multiple transmission scenarios, ranging from a single-channel QPSK baseline to a 16-channel WDM system employing both QPSK and 16QAM modulation formats. Key system parameters—including launch power, local oscillator power, bit rate, and fiber length—are jointly optimized, while performance is rigorously assessed in terms of bit error rate (BER), Q-factor, and maximum transmission reach. The results demonstrate a clear performance degradation trend driven by both spectral efficiency scaling and cryptographic payload expansion. The single-channel QPSK system achieves a maximum reach of 203 km, which decreases to 194 km in the 16-channel WDM QPSK configuration due to inter-channel interference and nonlinear effects. In contrast, the 16-channel WDM 16QAM system exhibits a significantly reduced reach of 103 km, reflecting its heightened sensitivity to noise, chromatic dispersion, and fiber nonlinearities. Furthermore, increased payload size associated with PQDS schemes is shown to exacerbate transmission impairments by extending frame duration and intensifying inter-channel interactions. These findings identify PQDS-induced overhead as a critical system-level constraint that directly governs transmission efficiency, scalability, and performance limits. The study highlights the necessity of cross-layer co-design strategies, where cryptographic mechanisms and physical-layer parameters are jointly optimized to enable efficient, reliable, and quantum-safe coherent optical communication systems. Full article

The contemporary media landscape has sustained a substantial transformation with the rise of AI-driven algorithmic platforms that enable activist organizations to produce and disseminate their own forms of political communication and campaigns. This study examines the YouTube channel of Code Pink, a prominent U.S.-based anti-war and social justice organization, to explore how activist media practices intersect with contemporary forms of journalism. Over a one-month period, video transcripts from the organization’s YouTube channel were analyzed using NVivo 15, employing a hybrid qualitative approach that combined inductive and deductive coding. Deductive codes were informed by sustained observation of the channel over one year (short and long videos on YouTube, TikTok, and X), supplemented by engagement with relevant news coverage, while inductive coding followed grounded theory principles, allowing themes to emerge directly from the transcripts. Large Language Models (LLMs) were employed as exploratory analytic tools to support AI-assisted qualitative analysis, complementing manual coding processes. The analysis focuses on how Code Pink frames political events and U.S. foreign policy through confrontational interviews, protest documentation, and the dissemination of commentary to online audiences. Findings suggest that the organization’s video content operates simultaneously as political activism, protest performance, and quasi-journalistic reporting. Activists frequently adopt journalistic techniques—including interviewing political figures, providing on-the-ground commentary, and framing narratives around public accountability—while also advancing explicit ideological positions that challenge dominant media narratives. The study highlights how platform-based activist media blurs the boundaries between journalism, advocacy, and political performance, contributing to the construction of alternative public narratives in the digital age. Full article

This study examines how AI-enhanced motion capture (AI-MoCap) mediates the preservation, transmission, and re-creation of Chinese shadow puppetry as performative intangible cultural heritage. Through a state-of-the-art review and comparative analysis of three representative application models—technology-driven, culturally integrated, and entertainment-oriented—the paper explores how AI-MoCap supports the digitization of performative techniques while reshaping modes of cultural presentation and interaction. Cross-case comparison highlights recurring tensions between technical standardization and cultural authenticity while also indicating possibilities for symbolic reconstruction, contextual continuity, and ethically grounded design. Based on this comparison, the paper develops a dual-channel inheritance framework—“perception–symbol” and “design–performance”—and treats cultural resolution and digital ethics as analytical and normative principles for resisting algorithmic homogenization. Rather than functioning only as a digitization tool, AI-MoCap can be understood as a mediating mechanism whose cultural value depends on how it remains embedded in community-based performative logics, symbolic systems, and ethical boundaries. The resulting framework offers transferable guidance for future research, curation, training, and policy discussion in the digital safeguarding of performance-based heritage. Full article