Search Results (2,706)

The increasing interconnectivity and digital transformation of Communication, Navigation, and Surveillance (CNS) systems have expanded their attack surface, rendering traditional perimeter-based security models inadequate for protecting these critical infrastructures. Zero Trust Architecture (ZTA), founded on the principle of “never trust, always verify,” offers a paradigm shift towards continuous, context-aware security. This paper presents a literature review investigating the application of ZTA principles to secure modern CNS ecosystems, following the guidelines of the International Civil Aviation Organization (ICAO) through its Cybersecurity Strategy and Plan. We analyze the alignment of ZTA core tenets—such as least-privilege access, micro-segmentation, and continuous authentication—with the unique operational requirements of CNS systems. This paper also presents a cybersecurity framework, under development within the Future Communications Digital Infrastructure (FCDI) project of the SESAR JU program, which aims to assist CNS stakeholders in collaboratively identifying cybersecurity threats within their scope of responsibility. The review critically examines implementation challenges for specific CNS subsystems: secure aeronautical communications (e.g., LDACS), resilient PNT (Positioning, Navigation, and Timing) services, and integrated surveillance networks (e.g., ADS-B, multilateration). Furthermore, we identify and evaluate domain-specific challenges, including integration with legacy avionics and ground systems, managing stringent latency and reliability constraints, and protecting against sophisticated threats targeting supply chains and data fusion processes. By synthesizing current research and practical deployment insights, this review aims to provide a foundational reference for aerospace engineers, cybersecurity specialists, and policymakers, offering a roadmap to enhance the cyber-resilience of vital CNS infrastructure in an era of evolving digital threats. Full article

The application of Internet of Things (IoT) technology to Digital Twin (DT) in smart manufacturing has opened significant opportunities for real-time monitoring, predictive maintenance, and closed-loop control; however, the inherent latency that exists in these architectures (the temporal gap between a physical event and its reflection in a digital model) remains one of the most significant and least systematically understood barriers to fulfill its full potential. This paper aims to propose a formal four-layer taxonomy of latency sources in IoT-based Digital Twin systems for smart manufacturing and to review the current approaches and tools that are available for their measurement. The PRISMA protocol has been used to perform a systematic literature review, where 58 primary survey studies published between 2020 and 2026 were extracted from IEEE Xplore, Elsevier Scopus, Google Scholar and arXiv, with all the studies being coded along six dimensions (architectural layer, application domain, latency metrics reported, evaluation methodology, quantitative impact, and enabling technologies). The proposed taxonomy presents 28 different types of latencies under four layers: (L1) network, (L2) compute, (L3) data, and (L4) end-to-end (E2E), whose magnitudes vary from 0.1 ms for local network propagation to tail latencies above 500 ms in production (P99). Three categories and three cross-layer interaction patterns are formalized here and are absent from prior partial taxonomies. Among the most promising results is the finding that several high-impact interventions require no infrastructure investment: a protocol migration from Modbus to WebSocket reduces telemetry latency by 32%, while Age of Information-aware synchronization and clock drift correction deliver substantial data layer gains through software updates alone, yet remain underutilized. The review identifies a systematic under-reporting of tail-latency percentiles across the corpus, the lack of a cross-protocol jitter benchmark, and a predominance of simulation-based evaluation over real-hardware measurement. The systematic review contributions of this paper (the formal four-layer taxonomy, the proportional metric audit across the 58 papers, and the formalization of three cross-layer interaction patterns) are derived from cross-corpus analysis. The investigation also identifies three open research directions (a standardized manufacturing IoT-DT benchmark, cross-layer joint optimization frameworks, and wireless TSN validation on real manufacturing testing grounds) that together form a well-organized and practical basis to advance both the science and the application of ultra-low-latency Digital Twin technology in the industrial field. Full article

Vitis vinifera L. × Vitis labrusca L. cv. Black Queen (BQ) is a hybrid cultivar with oenological potential in subtropical climates, yet its sensory structure remains insufficiently systematized. This study aimed to construct an integrated sensory framework by merging two Balanced Complete Block Design (BCBD) datasets into a unified database and developing a structured descriptor reduction workflow to address multicollinearity and redundancy. The resulting “BQ Lexicon v.0” comprised nine Quantitative Descriptive Analysis (QDA) attributes and twelve check-all-that-apply (CATA) descriptors. Based on this optimized dataset, an Artificial Neural Network (ANN) model was developed to predict overall liking (OL), achieving a satisfactory performance (R²(train) = 0.70 and R²(validation) = 0.74). Three-dimensional response surface visualization further illustrated non-linear relationships as a process monitor, indicating sourness as a primary negative driver of acceptance and revealing interactive and synergistic effects between tannin, sweetness, and aroma. These findings demonstrate that integrating structured data management with machine learning can enhance sensory modeling efficiency. Ultimately, the validated BQ Lexicon v.0 and the aligned data framework establish a reliable foundation for future oenological research in Black Queen grape. This structured approach effectively resolves the challenges of integrating distributed sensory datasets, while offering practical insights for targeted winemaking strategies. Full article

Aerobiology, the science that studies atmospheric biological particles (including pollen, fungal spores, bacteria, and viruses), has undergone a profound transformation from a descriptive, observational discipline into a predictive, data-driven field, thanks to advances in statistical methods and environmental sensing technologies. Early research, based on classical statistical methods such as descriptive analysis, correlation analysis, and linear regression, established a fundamental understanding of seasonal dynamics and environmental relationships. However, the inherent complexity of aerosol biological systems—characterized by nonlinear interactions, spatiotemporal variability, and multiscale processes—has spurred the adoption of modern statistical techniques. These techniques include time-series analysis, generalized linear and additive models, spatial statistics, Bayesian inference, machine learning, and data assimilation, often combined with high-resolution environmental monitoring and sensor networks. In recent years, hybrid modeling approaches have emerged, combining mechanistic understanding of atmospheric transport and biological emissions processes with data-driven learning to improve the accuracy, robustness, and interpretability of predictions. This review comprehensively compares classical, modern, and hybrid statistical methods in air biology, exploring their theoretical foundations, practical applications, and inherent limitations. Furthermore, this review highlights emerging paradigms such as uncertainty quantification, causal inference, digital twins, and AI-driven real-time prediction systems. It also discusses challenges, including data heterogeneity, model interpretability, and cross-regional portability. By treating aerobiology as a complex adaptive environmental–biological system, this study highlights statistical methods that link observations to mechanisms and advance scalable, reliable, systems-oriented prediction frameworks for future research and applications. Full article

Smart tourism destinations, embedded by the internet and information and communication technologies, have been improving tourists’ experiences and connectivity. However, Destination Management Organisations (DMOs) still lack knowledge of how digital technologies can enhance their role and bring greater competitive advantage to destinations. In this sense, this study aims to develop an integrated smart tourism destination management ecosystem model that clarifies the relationships between digital technologies, managerial functions, benefits and implementation barriers within the broader smart city context. The study adopts a mixed-review design, combining bibliometric analysis and a systematic literature review. Bibliometric mapping was conducted using VOSviewer to analyse co-occurrence networks, thematic clusters and research trends. At the same time, the systematic review, with a systems thinking approach, enabled an in-depth qualitative examination of technological applications, managerial roles and governance implications. Data was gathered from 29 Scopus-indexed articles. The analysis identifies key benefits, including enhanced visitor experiences, improved decision-making and increased destination competitiveness, alongside persistent barriers related to governance, digital literacy, interoperability and cybersecurity. Based on these findings, the study proposes a conceptual ecosystem model that illustrates how DMOs can orchestrate digital technologies to support smart, sustainable and adaptive destination management. This research contributes to the smart tourism and smart cities literature by integrating bibliometric insights with a systems thinking perspective to develop a holistic destination management ecosystem model. Unlike prior reviews that address technologies or outcomes in isolation, this study offers a structured and actionable framework that advances theoretical understanding of smart tourism destinations while providing practical guidance for DMOs engaged in digital transformation. Full article

Background/Objectives: Insomnia is one of the most prevalent and persistent symptoms among patients with breast cancer, yet it remains under-recognized and undertreated in routine clinical practice. Beyond its impact on sleep quality, insomnia is increasingly understood as a multidimensional condition involving neurobiological, psychological, and behavioral mechanisms, closely intertwined with cancer-related stress and psychiatric comorbidities. This narrative review aims to provide a comprehensive and integrative overview of insomnia in breast cancer, focusing on its epidemiology, pathophysiological underpinnings, neuropsychiatric correlates, and clinical implications, while highlighting gaps in current research and management. Methods: A narrative review of the literature was conducted, including studies published in major medical databases (PubMed, Scopus, and Web of Science) up to 2025. Relevant articles addressing insomnia, sleep disturbances, psychiatric symptoms, and neurobiological mechanisms in breast cancer populations were selected and synthesized. Results: Insomnia affects a substantial proportion of breast cancer patients across the disease trajectory, from diagnosis to survivorship. Its etiology is multifactorial, involving dysregulation of the hypothalamic–pituitary–adrenal axis, inflammatory processes, and circadian rhythm, as well as treatment-related factors such as chemotherapy, endocrine therapy, and menopausal symptoms. Insomnia frequently co-occurs with depression, anxiety, fatigue, and pain, forming symptom clusters that significantly impair quality of life and may influence clinical outcomes. Emerging evidence supports a bidirectional relationship between insomnia and psychiatric vulnerability, suggesting a shared neurobiological substrate within the brain–body stress axis. Conclusions: Insomnia in breast cancer should be conceptualized as a neuropsychiatric condition embedded within a broader stress-related symptom network rather than as an isolated sleep disturbance. Improved screening, interdisciplinary management, and the integration of evidence-based interventions such as cognitive behavioral therapy for insomnia are essential. Research should focus on personalized and mechanistically informed approaches to better address this highly prevalent yet insufficiently managed condition. Full article

The processing of two-dimensional (2D) spectral images constitutes a critical and multifaceted discipline in contemporary astronomical data analysis. As spectroscopic instruments evolve towards higher multiplexing, resolution, and sensitivity, the raw 2D data captured by detectors present increasingly complex challenges that transcend simple one-dimensional extraction. This review provides a systematic and comprehensive examination of the methodological evolution in this field over the past two decades. It gathered relevant studies by searching mainstream academic repositories and general search engines with the core keyword ‘2D Spectral Image’, and selected qualified references according to accessibility and research relevance. We categorize the landscape into three major paradigms: (1) physics-based modeling and algorithmic correction techniques for geometric distortion, scattered light, and sky background; (2) data-driven machine learning and deep learning approaches for image correction, spectral classification, and faint signal detection; and (3) the development of open-source software pipelines that democratize advanced processing. A central contribution of this review is a detailed comparative analysis of the performance metrics, underlying assumptions, and practical limitations of prominent algorithms. We highlight the transformative impact of convolutional neural networks (CNNs) and vision transformers (ViTs) on tasks such as celestial object classification and exoplanet detection, while also acknowledging the enduring importance of robust physical models for calibration and uncertainty quantification. The discussion culminates in an assessment of persistent challenges—including computational scalability, model generalizability, and interpretability—and outlines promising future directions at the intersection of AI, statistical inference, and large-scale survey science. Full article

Silver nanowire (AgNW) networks have attracted significant attention as leading candidates for flexible transparent electrodes owing to their unique combination of high electrical conductivity, optical transparency, and mechanical compliance. This review presents an overview of recent developments in AgNW-based transparent electrode technologies, with particular emphasis on strategies to improve network conductivity and long-term reliability, including junction engineering, surface modification, encapsulation approaches, and composite structure design. Representative applications in flexible optoelectronic systems, such as organic light-emitting devices, transparent heating elements, and electrochromic platforms, are also discussed. Finally, current challenges and future research directions toward scalable manufacturing and practical implementation of high-performance AgNW electrodes are outlined. Full article

High dynamic range (HDR) image reconstruction from a single low dynamic range (LDR) input remains an important problem for computational photography, particularly when practical deployment on consumer-grade hardware is considered. With the increasing availability of hardware supporting HDR, public demand for capturing and viewing HDR images has grown significantly. Recent research has explored deep learning-based approaches to reconstruct HDR images from low dynamic range (LDR) inputs by extracting regional pixel features or leveraging the camera response function (CRF) for model training. Many of these approaches employ Convolutional Neural Network (CNN) architectures and utilize skip connections to preserve learned information. Nevertheless, the configuration-level effects of data augmentation in HDR reconstruction remain insufficiently discussed. Existing CNN-based approaches, such as HDRCNN, HDRUNet, and ExpandNet, have demonstrated promising reconstruction ability, but they may involve a heavy backbone architecture, a long training time, or a limited discussion of how preprocessing configurations affect reconstruction performance. This study presents an engineering-oriented HDR reconstruction framework derived from HDRCNN, focusing on practical efficiency, structural fidelity, and training feasibility. The proposed framework introduces three modifications: (1) a configuration-level comparison of composite data augmentation settings, including unsharp masking, denoising, Gaussian blur, and brightness–contrast adjustment; (2) the replacement of the original VGG16 backbone with a ResNet50-based encoder enhanced with attention blocks and squeeze-and-excitation (SE) blocks for improved multi-scale feature extraction and channel-wise recalibration; and (3) the integration of mixed-precision training with cosine annealing learning-rate scheduling to reduce computational cost. Experimental results on the SI-HDR dataset show that the best composite augmentation configuration improves PSNR from 19.05 dB to 22.10 dB and SSIM from 0.6444 to 0.7714 without increasing the training time. Compared with the original VGG16-based HDRCNN setting, the ResNet50-based model reduces training time while improving SSIM from 0.2705 to 0.8512. Under the adopted comparison protocol, the proposed model achieves the shortest training time and slightly higher PSNR than HDRUNet, while HDRUNet retains a higher SSIM. This indicates a trade-off among pixel-wise fidelity, structural similarity, and computational efficiency. The current evaluation is limited by a small test setting, composite rather than operation-level augmentation analysis, and the use of PSNR and SSIM only; therefore, future work should include full benchmark evaluation, additional perceptual/HDR-specific metrics, and controlled component-level ablation studies. Full article

This paper presents a comparative analysis of selected economic indicators within the Italian railway passenger transport sector during the 2010–2024 period. Characterized by high-speed rail (HSR) saturation and advanced market liberalization, the Italian railway system serves as a reference model for investigating structural shifts within mature transport networks. The study aims to quantify the dynamics of transport performance through a synthesis of multiple analytical dimensions: passenger volume, transport performance (passenger-kilometers), modal split, average transport distances, and indicators of general and dynamic population mobility. The methodological framework is based on the application of chain and base indices, enabling the precise identification of cyclical fluctuations, exogenous disruptions (primarily the impact of the COVID-19 pandemic), and the subsequent degree of systemic resilience. The analysis suggests a significant shift in demand composition after 2014, characterized by an expansion of short- and medium-distance segments alongside a transformation in travel behavior. The research findings determine the correlation between infrastructure investment and the actual positioning of rail transport within a multimodal system. This work provides an analytical foundation for strategic planning in transport policy and sustainable mobility within the context of European transport integration. Moreover, these insights are practically applicable for transport operators and planners in forecasting demand, optimizing network capacity, and enhancing infrastructure resilience against future exogenous shocks. Full article

Time-delay systems (TDSs) arise in many engineering applications where sensing, actuation, computation, transport, or communication delays affect closed-loop stability and performance. Classical control methods, including predictor-based control, Lyapunov–Krasovskii functional approaches, robust control, model predictive control, and adaptive control, provide rigorous theoretical foundations for delay compensation and stability analysis. However, their effectiveness may be limited when the system is nonlinear, uncertain, poorly modeled, or subject to unknown and time-varying delays. In recent years, artificial intelligence (AI)-based methods, such as neural networks, fuzzy systems, deep learning, and reinforcement learning, have attracted increasing attention for their capabilities in learning, approximation, prediction, and adaptation. This paper presents a comprehensive review and bibliometric analysis of control strategies for TDSs, with an emphasis on the interactions among classical, AI-based, and hybrid methods. Publications indexed in the Web of Science database from 2010 to 2025 are analyzed using bibliometrix and VOSviewer to identify publication trends, influential contributors, collaboration patterns, citation structures, and thematic evolution. In addition, a unified framework is proposed to classify TDS control strategies into classical, AI-based, and hybrid categories. The results show that classical stability and robustness analysis remain central to the field, while AI-based and hybrid methods are increasingly used to address nonlinearities, uncertainties, communication delays, and real-time implementation challenges. Finally, key research gaps and future directions are discussed, including stability-guaranteed learning, learning-based delay compensation, interpretable AI control, benchmarking, and practical deployment in cyber-physical, robotic, aerospace, and networked systems. Full article

With the rapid proliferation in communication devices and the expansion of applications, future sixth-generation (6G) networks are expected to enable a truly connected world. They will allow large-scale use cases, such as the Internet of Things (IoT) and unmanned aerial vehicles (UAVs), providing significantly faster and more innovative services ubiquitously. However, challenges remain, particularly in security. The growing number of devices and increased connectivity may lead to a larger attack surface. Many emerging technologies are actively addressing these security and privacy concerns, ensuring that we can benefit from the advantages of 6G networks and applications without falling victim to malicious attacks. In this paper, we conduct a comprehensive literature review of emerging technologies for secure communication in 6G networks, including artificial intelligence (AI) and machine learning (ML), blockchain technology, quantum-safe communication, and physical-layer security. First, we discuss the architecture of 6G networks from a security perspective. Second, we review existing surveys on 6G security issues and provide a quantitative analysis to identify research gaps, including technology-driven silos and domain fragmentation. Third, we develop a hierarchical taxonomy of security challenges and attacks in 6G networks, covering physical-layer attacks, network-level threats, device vulnerabilities, data privacy concerns, and emerging application-specific risks. We then examine the roles of key enabling technologies and present a mapping between security threats and corresponding technological solutions, along with a unified evaluation framework to facilitate cross-technology comparison. Furthermore, we propose an integrated multi-technology security framework and discuss practical deployment challenges by bridging the gap between simulation-based studies and real-world implementations. Finally, we outline concrete future research directions for advancing secure 6G communication systems. Full article

Post-war reconstruction in Aleppo requires more than replacing damaged buildings; it demands an understanding of the city’s historically layered urban fabrics, their differing socio-spatial logics, and their unequal capacities for recovery. Following severe conflict-related destruction during the Syrian civil war, particularly between 2012 and 2016, and the additional impact of the February 2023 earthquake, Aleppo’s recovery is further complicated by the heritage significance of its Ancient City, inscribed on the UNESCO World Heritage List in 1986 and included on the List of World Heritage in Danger since 2013. This study examines how urban morphology can guide reconstruction through a comparative analysis of four neighborhoods representing major phases of Aleppo’s development: Jdaideh, Azizieh, Mohafaza, and Jabal Badro. Using a qualitative historical–morphological approach, the research analyzes figure–ground relations, street-network structure, degrees of transition between public, semi-public, semi-private, and private spaces, and landmark–node systems to identify the spatial characteristics, temporal persistence, and planning meaning of each district. The findings show that Aleppo is not a homogeneous urban system but a city composed of distinct fabrics with different strengths, vulnerabilities, and reconstruction needs. The comparison further demonstrates that density alone is not an adequate indicator of urban quality or resilience. The study concludes that reconstruction should be based on fabric-specific strategies, including preservation-sensitive rehabilitation, reinforcement of public nodes, balanced connectivity, governance-aware phasing, and incremental upgrading. Urban morphology is therefore proposed as a practical, but not exhaustive, framework for context-sensitive recovery in conflict-affected and historically layered cities. Full article

Protein–protein interactions (PPIs) represent one of the major factors determining structure, function and quality in food products, especially in the case of industrial processing. Within complex food matrices, the structural and physical behavior of food components is controlled by PPIs that determine aggregation behavior, network formation, phase stability, and structural integrity and are thus directly related to the stability of the final product and how well a product may perform during a process. Recent developments in analytical techniques have facilitated the elucidation of PPIs and their application in activity-induced structural changes, in particular during thermal, non-thermal, enzymatic, and mechanical processes. In lieu of providing an exhaustive summary, this review synthesizes research evidence and findings related to measuring PPIs from main food systems, namely dairy, meat, cereal and plant-based products. The impact of different processing methods on PPIs and related quality characteristics including structure, stability and functional activity is critically assessed. Knowledge gaps and methodological limitations (in particular concerning laboratory scale industrial processes) are highlighted. By combining mechanistic considerations with practical performance considerations, this review allows us to rationalize the improvement of food processing strategies and to develop protein-based foods with better quality and performance stability. Full article

Peat soils play a key role in the global terrestrial carbon pool, water regulation, and ecosystem stability, making them central to environmental protection and climate resilience policies. This study offers a thorough bibliometric mapping and scientific overview for the development path and intellectual structure of peat soil research from 2015 to 2025. Using the Scopus database, 1558 records were systematically analyzed with VOSviewer and an R-package to reveal publication trends, country/region collaboration networks, keyword co-occurrence clusters, and citation structures. Peat research is increasingly focused on carbon storage, peatland degradation and restoration, greenhouse gas emissions, land-use change, and climate mitigation. High citation rates of 651 in Nature Climate Change show strong interdisciplinary integration of soil science, ecology, hydrology, and climate science. Major contributions come from regions with extensive peatlands, like Southeast Asia and Northern Europe, highlighting their global climate importance. However, there are still missing links remaining between scientific research and practical peatland management and restoration. Future research should focus on long-term field studies, socio-ecological peatland governance, and nature-based solutions to enhance climate resilience. This study serves as a reference for researchers, environmental managers, and policymakers promoting sustainable peat soil management amid global environmental change. Full article