Search Results (1,287)

Multimodal sentiment analysis (MSA) faces key challenges such as incomplete modality inputs, long-range temporal dependencies, and suboptimal fusion strategies. To address these, we propose MGMR-Net, a Mamba-guided multimodal reconstruction and fusion network that integrates modality-aware reconstruction with text-centric fusion within an efficient state-space modeling framework. MGMR-Net consists of two core components: the Mamba-collaborative fusion module, which utilizes a two-stage selective state-space mechanism for fine-grained cross-modal alignment and hierarchical temporal integration, and the Mamba-enhanced reconstruction module, which employs continuous-time recurrence and dynamic gating to accurately recover corrupted or missing modality features. The entire network is jointly optimized via a unified multi-task loss, enabling simultaneous learning of discriminative features for sentiment prediction and reconstructive features for modality recovery. Extensive experiments on CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets demonstrate that MGMR-Net consistently outperforms several baseline methods under both complete and missing modality settings, achieving superior accuracy, robustness, and generalization. Full article

This study evaluates the impact of digital systems and IT audit frameworks on tax performance and integrity within tax administrations. Using international data from organizations like the World Bank, OECD (Organisation for Economic Co-operation and Development), and IMF (International Monetary Fund), the research examines the relationship between tax revenue as a percentage of GDP, digital infrastructure, corruption perception, e-government development, and cybersecurity readiness. Quantitative analysis, including correlation, regression, and clustering methods, reveals a strong positive relationship between digital maturity, e-governance, and tax performance. Countries with advanced digital governance systems and robust IT audit frameworks, such as COBIT, tend to show higher tax revenues and lower corruption levels. The study finds that e-government development and anti-corruption measures explain over 40% of the variance in tax performance. Cluster analysis distinguishes between digitally advanced, high-compliance countries and those lagging in IT adoption. The findings suggest that digital transformation strengthens fiscal integrity by automating compliance and reducing human contact, which in turn mitigates bribery risks and enhances fraud detection. The study highlights the need for adopting international best practices to guide the digitalization of tax administrations, improving efficiency, transparency, and trust in public finance. Full article

This study presents a lightweight autoencoder-based approach for anomaly detection in digit recognition using federated learning on resource-constrained embedded devices. We implement and evaluate compact autoencoder models on the ESP32-CAM microcontroller, enabling both training and inference directly on the device using 32-bit floating-point arithmetic. The system is trained on a reduced MNIST dataset (1000 resized samples) and evaluated using EMNIST and MNIST-C for anomaly detection. Seven fully connected autoencoder architectures are first evaluated on a PC to explore the impact of model size and batch size on training time and anomaly detection performance. Selected models are then re-implemented in the C programming language and deployed on a single ESP32 device, achieving training times as short as 12 min, inference latency as low as 9 ms, and F1 scores of up to 0.87. Autoencoders are further tested on ten devices in a real-world federated learning experiment using Wi-Fi. We explore non-IID and IID data distribution scenarios: (1) digit-specialized devices and (2) partitioned datasets with varying content and anomaly types. The results show that small unmodified autoencoder models can be effectively trained and evaluated directly on low-power hardware. The best models achieve F1 scores of up to 0.87 in the standard IID setting and 0.86 in the extreme non-IID setting. Despite some clients being trained on corrupted datasets, federated aggregation proves resilient, maintaining high overall performance. The resource analysis shows that more than half of the models and all the training-related allocations fit entirely in internal RAM. These findings confirm the feasibility of local float32 training and collaborative anomaly detection on low-cost hardware, supporting scalable and privacy-preserving edge intelligence. Full article

Frequency-hopping multiple access is widely adopted to blunt narrow-band jamming and limit spectral disclosure in cyber–physical systems, yet its practical resilience depends on three sequence-level properties. First, balancedness guarantees that every carrier is occupied equally often, removing spectral peaks that a jammer or energy detector could exploit. Second, a wide gap between successive hops forces any interferer to re-tune after corrupting at most one symbol, thereby containing error bursts. Third, a no-hit zone (NHZ) window with a zero pairwise Hamming correlation eliminates user collisions and self-interference when chip-level timing offsets fall inside the window. This work introduces an algebraic construction that meets the full set of requirements in a single framework. By threading a permutation over an integer ring and partitioning the period into congruent sub-blocks tied to the desired NHZ width, we generate balanced wide gap no-hit zone frequency-hopping (WG-NHZ FH) sequence sets. Analytical proofs show that (i) each sequence achieves the Lempel–Greenberger bound for auto-correlation, (ii) the family and zone sizes satisfy the Ye–Fan bound with equality, (iii) the hop-to-hop distance satisfies a provable WG condition, and (iv) balancedness holds exactly for every carrier frequency. Full article

Small news media organizations are increasingly reshaping the news media system in Latin America. They are stepping into the role of watchdogs by investigating issues such as corruption scandals that larger outlets sometimes overlook. However, this journalistic work exposes both journalists and their organizations to a range of security threats, including physical violence, legal pressure, and digital attacks. In response, these outlets have developed coping strategies to manage and mitigate such risks. This article presents an exploratory study of the approaches adopted to protect information and data, ensure the safety and well-being of journalists, and maintain organizational continuity. Based on a series of in-depth interviews with leaders of award-winning news organizations for their investigative reporting, the study examines a shift from a competitive newsroom model to a collaborative approach in which information is shared—sometimes across borders—to support investigative reporting and strengthen security practices. We identify strategies implemented by small news organizations to safeguard their journalistic work and propose an integrative model of news safety encompassing the following three areas of security: physical, legal, and digital. This study contributes to the development of the newsafety framework and sheds light on safety practices that support media freedom. Full article

This paper presents the Green Watershed Index (GWI) methodology, focusing on the 17 sustainability indicators selected in the Razey watershed, NW Iran. Field surveys and data collection have provided the possibility of field inspection and measurement of the present condition of the watershed and the indicators taken. Based on the degree of compliance with the required process, each indicator was scored from 0 to 10 and classified into three categories: unsustainable, semi-sustainable, and sustainable. Using the Entropy method to assign weight to each indicator and formulating a proportional mathematical relationship, the GWI score for each sub-watershed was derived. Spatial changes regarding the selected indicators and, consequently, the GWI were detected in the study area. Development of water infrastructure, particularly in the upstream sub-watersheds, plays a great role in increasing the GWI score. The highest weight is related to environmental productivity (0.26), and the five indicators of water footprint, knowledge management and information quality system, landscape attractiveness, waste recycling, and corruption control have approximately zero weight due to their monotonous spatial distribution throughout sub-watersheds. Only sub-watershed R1 has the highest score (5.13), indicating a semi-sustainable condition. The rest of the sub-watersheds have unsustainable conditions (score below 5). Concerning the GWI, the watershed is facing a critical situation, necessitating the implementation of management and conservation strategies that align with the sustainability level of each sub-watershed. Full article

Sustainability in the non-alcoholic beverage industry requires effective metrics to assess environmental, social, and economic performance. However, the lack of standardised indicators for small and medium-sized enterprises (SMEs) hinders the implementation of sustainable strategies. This study aims to select a set of sustainability indicators for small and medium-sized non-alcoholic beverage industries in Brazil. Seventy-four indicators were identified based on the Global Reporting Initiative (GRI) guidelines, which were subsequently evaluated and refined by industry experts for prioritisation. Statistical analysis led to the selection of 31 final indicators, distributed across environmental (10), social (12), and economic (9) dimensions. In the environmental dimension, priority indicators include water management, energy efficiency, carbon emissions, and waste recycling. The social dimension highlights working conditions, occupational safety, gender equity, and impacts on local communities. In the economic dimension, key indicators relate to supply chain efficiency, technological innovation, financial transparency, and anti-corruption practices. The results provide a robust framework to guide managers in adopting sustainable practices and support policymakers in improving the environmental, social, and economic performance of small and medium-sized non-alcoholic beverage industries. Full article

Ensuring data integrity and adaptability in aircraft health monitoring (AHM) is vital for safety-critical aviation systems. Traditional digital twin (DT) and federated learning (FL) frameworks, while effective in enabling distributed, privacy-preserving fault detection, lack mechanisms to remove the influence of corrupted or adversarial data once these have been integrated into global models. This paper proposes a novel FL–DT–FU framework that combines digital twin-based subsystem modeling, federated learning for collaborative training, and federated unlearning (FU) to support the post hoc correction of compromised model contributions. The architecture enables real-time monitoring through local DTs, secure model aggregation via FL, and targeted rollback using gradient subtraction, re-aggregation, or constrained retraining. A comprehensive simulation environment is developed to assess the impact of sensor drift, label noise, and adversarial updates across a federated fleet of aircraft. The experimental results demonstrate that FU methods restore up to 95% of model accuracy degraded by data corruption, significantly reducing false negative rates in early fault detection. The proposed system further supports auditability through cryptographic logging, aligning with aviation regulatory standards. This study establishes federated unlearning as a critical enabler for resilient, correctable, and trustworthy AI in next-generation AHM systems. Full article

This study investigates whether institutional quality amplifies the emissions-reducing effect of energy efficiency in hydrocarbon-dependent economies. Addressing a gap in the energy–environment literature, it tests how governance conditions shape the effectiveness of technical mitigation strategies. Using panel data from six Gulf Cooperation Council (GCC) countries between 2000 and 2023, we estimate a fixed-effects model with interaction terms between energy intensity (as a proxy for efficiency) and institutional quality (proxied by Control of Corruption). The results show that energy efficiency is associated with lower CO₂ emissions, and this relationship is significantly moderated by institutional quality. We also analyze the emissions impact of natural gas consumption and identify a structural shift following the 2014 energy reforms: while gas use was positively associated with emissions before 2014, the post-reform period shows a weaker or reversed effect. Robustness checks using alternative governance indicators—Regulatory Quality and Government Effectiveness—confirm the moderating role of institutions. The study offers new empirical evidence on the energy–institution–environment nexus and introduces a novel interaction-based methodology suited to resource-rich economies undergoing institutional transition. Full article

This paper analyzes how two major economic downturns—a recession and a stagflation—affected convergence in the European Union (EU). Absolute and conditional convergence rates are estimated using ordinary least squares (OLS) semilog regressions based on cross-sectional data from 2004 to 2022. The study tests two hypotheses: there was no absolute convergence in the EU during either the recession or the stagflation period, and conditional convergence occurred during the recession but not during stagflation. The regression results indicate that neither hypothesis can be rejected. External variables—economic openness, inflation, and investment—were more influential during stable periods, whereas internal variables—debt, unemployment, and the control of corruption—had a greater impact during crises. These findings suggest that the EU was more institutionally prepared for the stagflation due to mechanisms developed after the financial crisis, but these tools proved less effective in addressing supply-side shocks. Full article

To address the challenges of image inpainting in scenarios with extensive or irregular missing regions—particularly detail oversmoothing, structural ambiguity, and textural incoherence—this paper proposes an Image Structure-Guided (ISG) framework that hierarchically integrates structural priors with semantic-aware texture synthesis. The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent structural maps through contextual feature propagation; (2) Structure-Constrained Texture Synthesis, wherein a multi-scale generator with hybrid dilated convolutions and channel attention mechanisms iteratively refines high-fidelity textures under explicit structural guidance. The framework introduces three innovations: (1) a hierarchical feature fusion architecture that synergizes multi-scale receptive fields with spatial-channel attention to preserve long-range dependencies and local details simultaneously; (2) spectral-normalized Markovian discriminator with gradient-penalty regularization, enabling adversarial training stability while enforcing patch-level structural consistency; and (3) dual-branch loss formulation combining perceptual similarity metrics with edge-aware constraints to align synthesized content with both semantic coherence and geometric fidelity. Our experiments on the two benchmark datasets (Places2 and CelebA) have demonstrated that our framework achieves more unified textures and structures, bringing the restored images closer to their original semantic content. Full article

In the past hundred years, three pontiffs have written apostolic letters to commemorate anniversaries relating to Dante: in 1921, Benedict XV marked the sixth centenary of the death of the great poet; in 1965, Paul VI judged it opportune to write on the occasion of the seventh centenary of his birth; and in 2021, Pope Francis added his voice to the numerous others wishing to honour the memory of the supreme Florentine poet on the seventh centenary of his death. Each letter is a product of its time: one hundred years ago, the Pope—still confined within the Vatican and refusing to recognise the Kingdom of Italy due to the Roman Question—addressed his text “to the beloved sons, professors and pupils of literary institutes and centres of higher learning within the Catholic world”; Paul VI, in full accord with the spirit of the Second Vatican Council and its vision of a Church seeking collaboration with the world, addressed his writing to Dante scholars more broadly, and within the same letter, together with other academic authorities, established the Chair of Dante Studies at the Catholic University of the Sacred Heart in Milan; Pope Francis today, in his outward-facing style of evangelisation, challenges everyone to (re)read Dante, whose teaching remains relevant seven hundred years after his death. Despite the differing political contexts and ecclesial agendas, Benedict XV, Paul VI, and Pope Francis are united on one point: Dante is a Christian poet—critical of the Church, certainly, but loyal to his faith and desirous of a religious institution that is more serious and less corrupt. This brief study presents the homage which the Church, today, seven centuries later, renders to this Poet—now widely recognised as a passionate witness of an arduous and active faith, in pursuit of justice and freedom. Full article

The relationship between economic progress and environmental quality remains a central focus in global sustainability discourse. This study examines the link between per capita economic growth and CO₂ emissions across 128 countries from 1996 to 2022, controlling for energy consumption, trade volume, and foreign direct investment (FDI) inflows. It also evaluates the role of governance quality—measured by regulatory quality and its volatility—while considering the globalization index as a confounding factor influencing CO₂ emissions. We test the Environmental Kuznets Curve (EKC) hypothesis, which suggests that emissions initially rise with income but decline after reaching a certain economic threshold. Our findings confirm the global presence of the EKC. The analysis further shows that trade openness, governance, and globalization significantly influence FDI inflows, with FDI, in turn, reinforcing institutional quality through improved governance and globalization indicators. However, in countries with weaker governance and regulatory frameworks, FDI tends to promote pollution-intensive industrial growth, lending support to aspects of the Pollution Haven Hypothesis (PHH). We find a significant departure in EKC explained by post-COVID governance and globalization compromises, which induced the environment towards the PHH phenomenon. These results highlight the need for context-specific policy measures that align economic development with environmental constraints. Full article

Atmospheric turbulence severely degrades high-resolution satellite videos through spatiotemporally coupled distortions, including temporal jitter, spatial-variant blur, deformation, and scintillation, thereby constraining downstream analytical capabilities. Restoring turbulence-corrupted videos poses a challenging ill-posed inverse problem due to the inherent randomness of turbulent fluctuations. While existing turbulence mitigation methods for long-range imaging demonstrate partial success, they exhibit limited generalizability and interpretability in large-scale satellite scenarios. Inspired by refractive-index structure constant ($C_n^2$) estimation from degraded sequences, we propose a physics-informed turbulence signature (TS) prior that explicitly captures spatiotemporal distortion patterns to enhance model transparency. Integrating this prior into a lucky imaging framework, we develop a Physics-Based Turbulence Mitigation Network guided by Turbulence Signature (TMTS) to disentangle atmospheric disturbances from satellite videos. The framework employs deformable attention modules guided by turbulence signatures to correct geometric distortions, iterative gated mechanisms for temporal alignment stability, and adaptive multi-frame aggregation to address spatially varying blur. Comprehensive experiments on synthetic and real-world turbulence-degraded satellite videos demonstrate TMTS’s superiority, achieving 0.27 dB PSNR and 0.0015 SSIM improvements over the DATUM baseline while maintaining practical computational efficiency. By bridging turbulence physics with deep learning, our approach provides both performance enhancements and interpretable restoration mechanisms, offering a viable solution for operational satellite video processing under atmospheric disturbances. Full article

In a global context characterized by budgetary pressures, aging populations, and accelerated economic transitions, the capacity of countries to mobilize stable and sustainable tax revenues represents a crucial pillar for maintaining macroeconomic stability and social cohesion. This research investigated the determinants of total tax revenues in the developed economies of the G7 group (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) during the period 2000–2022, employing both static and dynamic panel econometric approaches. The estimated model considered total tax revenues as the dependent variable, while the explanatory variables encompassed the main categories of government revenues: direct taxes (personal and corporate income), indirect taxes (consumption, trade, and other taxes), social contributions, grants, other non-tax revenues, and institutional quality indicators (regulatory quality and control of corruption). The empirical findings revealed that all tax components analyzed exert a positive and significant influence on total tax revenues, with particularly strong effects observed for consumption taxes, social contributions, and personal income taxes. Based on these results, the study provides policy recommendations aimed at diversifying revenue sources, balancing direct and indirect taxation, and broadening the tax base equitably. The study advances the literature on international taxation by offering an integrated and comparative analysis of the revenue structures in advanced economies, while also identifying relevant pathways for sustainable tax reforms in a dynamic global environment. Full article