Search Results (719)

Choline is an essential nutrient whose physiological importance has not yet been sufficiently recognized by many European nutrition authorities. Despite convincing evidence of its crucial role in liver lipid export, one-carbon metabolism, cell membrane integrity, and nervous system development, explicit dietary recommendations for choline are still lacking in most European countries. In contrast, its importance has long been recognized in the national guidelines of the United States, Australia, China, and other regions. The current and rapidly spreading dietary shifts toward plant-based and vegan diets—characterized by a lower proportion of animal foods, the main sources of choline—increase the risk of suboptimal intake in broad segments of the population. Given the considerable interindividual differences in endogenous choline biosynthesis, which are influenced by sex hormones, physical activity, nutrient interactions, and genetic polymorphisms, adequate dietary intake is essential to meet physiological needs, especially during periods of increased demand such as pregnancy, lactation, and high-performance sports. This narrative review summarizes the evidence for the essentiality of choline, outlines the rationale for deriving intake recommendations for different life stages, and identifies an urgent need for coordinated action by European nutrition societies to address the growing risk of population-wide undersupply. Full article

Safety monitoring of scaffolding operations is essential for preventing accidents in high-altitude construction. This study proposes an integrated computer vision and multi-criterion decision-making (MCDM) framework that combines object detection, pose estimation, Analytic Network Process (ANP) and ELECTRE III methods to evaluate safety risks of construction workers. Specifically, computer vision techniques are employed to extract objective visual evidence related to workers’ behaviors, protective equipment (PPE) usage, and working environments, which serve as the basis for subsequent safety risk quantification. A four-criterion system, including action risk, PPE compliance, working height, and structural integrity, is established. Weights are determined via the ANP, and risk ranking is conducted using ELECTRE III. Experiments on a self-built dataset achieved an mAP@0.5 of 92.3%, a segmentation IoU of 67.2%, and a pose OKS@0.5 of 89.6%. The evaluation results correlate strongly with expert assessments (Kendall’s τ = 0.79). The proposed framework effectively identifies unsafe behaviors and quantifies safety risks, providing reliable decision support for intelligent construction safety management. Full article

Historic linear corridors in living-heritage settings concentrate identity, everyday mobility, and visitor experience. Balancing authenticity, adaptability, and publicness therefore benefits from evidence that jointly characterizes long-term physical change, network accessibility, and eye-level interface conditions. Existing assessments often focus on façades or single time slices, leaving limited evidence that relates decades of built-fabric reconfiguration (changes in building footprints, street edges, and open-space fragmentation) to multi-scale accessibility and pedestrian-facing qualities. We propose an integrated and interpretable workflow for the Beishan Street corridor in the West Lake World Heritage core (Hangzhou) over 1929–2024. Scale-sensitive morphological metrics, multi-radius network measures (integration and centrality), and street-view semantic segmentation are aligned at corridor-segment resolution and examined together with segment-level functional intensity derived from POIs using transparent linear models. The results indicate a long-term shift from a lakeshore-led to a road-led spatial logic, followed by post-2000 stabilization near saturation. Average integration increases, while the high-integration tail becomes thinner. In connector-removal scenarios, the eastern segment shows a relative accessibility decline, and a central hinge node emerges as a vulnerability hotspot (bottleneck) where through-movement concentrates. Eye-level profiles differ by segment: the west exhibits maximal canopy and lower sky visibility, the center shows stronger continuous walls around compounds with intermittent forecourt openings, and the east is characterized by compact residential heritage frontage with low vegetation. Segment-level associations suggest that address and wayfinding density tends to co-occur with clearer frontages, wider sky cones, and stronger tree cover. Transportation-related and access/passage facilities tend to co-occur with higher ground-plane legibility, measured as wider and more continuous road and sidewalk surfaces. Medical and government clusters tend to co-occur with lower sky openness. Recommended actions include the following: (1) mesh-aware protection of key connectors and the hinge, (2) segment-specific targets for façade share and ground cues with planned punctuations, (3) tailored interface standards for institutional clusters, (4) scalable address and wayfinding systems, and (5) event staging that preserves effective roadway and sidewalk capacity. Full article

Croatia’s tourism market is highly exposed to digital platforms and peer-to-peer information flows, yet evidence on how Croatian users differentiate between promotional formats (digital channels, agency websites, traditional media and word-of-mouth) remains fragmented and rarely translated into actionable priorities. This study aims to identify the underlying dimensions of perceived promotional influence and to prioritize promotional formats for destination choice in Croatia by integrating PCA and the Analytic Hierarchy Process (AHP). An online survey (N = 299) was used to extract promotional dimensions via PCA and to test group differences by gender, age and primary information source, while AHP translated expert judgments into a comparative priority structure. Results consistently indicate that word-of-mouth is the most persuasive driver of destination choice, but its perceived importance varies significantly across demographic segments and information-source profiles. Younger respondents place greater emphasis on digital channels (especially social media and travel agency websites), whereas older respondents show higher reliance on traditional formats. The combined PCA–AHP approach provides a structured bridge between user perceptions and managerial prioritization, offering segment-specific guidance for more efficient allocation of promotional resources in Croatian destination marketing. Full article

Intellectual property (IP) is a cornerstone of sustainable industrial growth, yet unpredictable fluctuations in patent application filings pose a challenge to the administrative efficiency and sustainable governance of patent offices. This study aims to enhance strategic R&D governance by analyzing the seasonality of patent application filings using monthly data from the Republic of Korea (January 2001 to July 2024) and proposing a time series forecasting model that reflects this seasonality. To verify seasonal patterns, visual analyses (graphs, time series decomposition, and autocorrelation function plots) and the Kruskal–Wallis test were conducted. The results confirmed a clear 12-month seasonal pattern, characterized by a distinct “December Rush” at the end of each year. Based on these findings, we compared the autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) models, demonstrating that the SARIMA model offers superior predictive performance by effectively capturing these cyclical fluctuations. Furthermore, by segmenting data into private and public R&D sectors, we observed that private R&D exhibits more pronounced seasonal volatility, necessitating differentiated management strategies. This study highlights the critical role of seasonality in forecasting patent volumes and provides a data-driven framework for sustainable governance, offering actionable insights for optimizing resource allocation and policy support in the innovation ecosystem. Full article

The market for remanufactured products made from marine plastic waste is expanding rapidly, but the recycling rate of this waste remains strikingly low. This disconnect forces conventional plastic recycling firms to make a consequential strategic choice: enter the marine plastic recycling supply segment by expanding to build market power or enter by competing as a specialized supplier. To examine this trade-off, this paper develops a two-period game-theoretic model that contrasts entry strategies and performance under monopolistic and competitive market structures. We derive and compare equilibrium pricing, quantities, and profits for the relevant supply chain participants in both settings and then characterize the conditions under which one entry mode dominates the other. The results indicate that neither the preferred entry strategy nor the profitability that follows is driven by a single parameter. Instead, outcomes are shaped by the joint effects of consumer tastes, remanufacturing costs, and the scale of capacity investment cost required for entry. When consumers show a stronger preference for conventional remanufactured products, a supplier pursuing monopolistic expansion can earn higher profits by offering a more flexible product portfolio. By contrast, when the cost of remanufacturing marine plastics and the associated capacity investment cost are relatively low, the environment favors a specialized, competitively oriented entry strategy. Profit allocation within the supply chain is also closely tied to remanufacturer costs: as these costs fall, suppliers are able to appropriate a larger share of total profits. Overall, the analysis provides a theoretical basis for entry decisions in the emerging marine plastic recycling industry and offers actionable guidance for firms facing different demand and cost conditions across market structures. Full article

Temporal action detection (TAD) aims to localize and recognize action instances in untrimmed videos, and serves as a key component in practical intelligent electronic systems such as smart video surveillance and real-time human–machine interaction. In these scenarios, accurate temporal localization is essential for reliable event understanding and downstream decision-making in edge computing and real-time streaming scenarios. To handle long video durations and diverse action dynamics, existing methods typically rely on hierarchical temporal feature integration to capture multi-scale contextual information. However, such integration often leads to intra-segment inconsistency and boundary ambiguity, as indiscriminate temporal smoothing across scales degrades segment coherence and blurs critical boundary cues. In this work, we propose FreqAct, a multi-frequency feature fusion framework that explicitly models complementary low-frequency and high-frequency temporal components within hierarchical representations. Specifically, low-frequency modulation suppresses undesired temporal fluctuations to stabilize segment-level representations, while high-frequency enhancement preserves boundary-sensitive cues essential for precise localization. Furthermore, we introduce a boundary-aware regression loss to emphasize learning at action boundaries and an intra-segment consistency regularization to encourage coherent predictions within each action instance. Extensive experiments on THUMOS14 and ActivityNet1.3 demonstrate that FreqAct outperforms state-of-the-art TAD methods, further highlighting its effectiveness and practical potential for real-world electronics applications. Full article

Plastic mulching is widely used in arid and semi-arid cotton systems to improve soil hydrothermal conditions and water–nutrient use efficiency. However, residual mulch and its potential contribution to microplastic inputs pose growing environmental and soil-quality risks, highlighting the need for high-resolution and automated approaches to support plastic waste management, targeted retrieval, and precision field operations. Taking a mulched cotton field in Alar, Xinjiang, as the study area, this study proposes a novel plastic mulch extraction method that integrates Unmanned Aerial Vehicle (UAV)-based hyperspectral imagery with deep learning semantic segmentation. The Jeffries–Matusita (JM) distance was employed to select highly separable optimal bands and their combinations for discriminating plastic mulch, bare soil, and cotton canopy, which were then used to drive UNet, DeepLabV3+, and PSPNet models for plastic mulch mapping. The results indicate that the PSPNet model driven by the 402 nm single-band reflectance, Normalized Difference Index (NDI) (861 nm, 410 nm), and NDI (757 nm, 676 nm) achieved the best performance for plastic mulch identification (Intersection over Union (IoU) = 80.28%), significantly outperforming the RGB-based model (IoU = 76.51%). This study enables accurate, spatially explicit assessments of residual mulch, providing actionable evidence for plastic waste monitoring and management, while supporting sustainable agriculture and precision farmland management. Full article

Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of Vision Mamba architectures, (ii) micro-architectural advancements such as adaptive scan strategies and hybrid SSM formulations, (iii) macro-architectural integrations, including CNN–Transformer–Mamba hybrids and frequency-domain adaptations, (iv) rigorous benchmarking against state-of-the-art methods in multiple application tasks, such as object detection, semantic segmentation, change detection, etc. and (v) critical analysis of unresolved challenges with actionable future directions. By bridging the gap between SSM theory and remote sensing practice, this survey establishes Mamba as a transformative framework for remote sensing analysis. To our knowledge, this paper is the first systematic review of Mamba architectures in remote sensing. Our work provides a structured foundation for advancing research in remote sensing systems through SSM-based methods. We curate an open-source GitHub repository to foster community-driven advancements. Full article

The effectiveness of Machine Learning (ML)-based Network Intrusion Detection Systems (NIDS) is critically hampered by the scarcity of realistic and up-to-date malware traffic datasets. To address this gap, we present an automated platform for generating real-world malware traffic datasets. Our solution leverages a production-environment honeynet (T-Pot), deployed within a university network and segmented via a secure WireGuard VPN, to capture live attacks using high-interaction honeypots (Dionaea, Cowrie, ADBhoney). A fully automated pipeline handles traffic capture, transfer, filtering based on honeypot logs, and malware analysis (VirusTotal, VxAPI). The output is the IPN-UAN-23 dataset—a curated, labeled corpus of malicious network traffic. This platform functions as a vital automated security tool, providing the continuous stream of actionable intelligence required to develop and refine robust ML-based NIDS within a DevSecOps lifecycle. Full article

Conventional Security Operations Center (SOC) solutions struggle to process representative operational alert streams efficiently and adapt to evolving cyber threats, highlighting the need for automated, intelligent threat detection and prioritization. This study presents a custom AI-driven framework that leverages unsupervised learning techniques to support SOC analysts in cyber threat detection, anomaly identification, and alert prioritization. The framework applies several clustering methods: HDBSCAN, DBSCAN, KMeans, and Gaussian Mixture Models for alert segmentation, and integrates anomaly detection using LOF and Isolation Forest, complemented by semi-supervised detection via One-Class SVM. Using textual, categorical, and numerical features from Wazuh alerts across three datasets, the system performs clustering and anomaly detection in the original high-dimensional feature space, with UMAP applied solely for two-dimensional visualization. HDBSCAN consistently produced well-separated clusters with effective noise detection, while, Isolation Forest evaluated via 10-fold cross-validation exhibited stable anomaly flagging and clear score separation across both cyber alert event data and synthetic threat injection experiments. Furthermore, the framework formulates a composite priority ranking that integrates anomaly severity, cluster rarity, and SOC contextual weighting, yielding actionable alert rankings. An interactive, analyst-centric dashboard enables SOC teams to explore top alerts, clusters, associated MITRE techniques, priority rankings, and geolocation data, providing insights while preserving human oversight. Overall, the proposed system transforms complex alert streams into structured insights, enhancing SOC situational awareness, decision support, and operational efficiency. Full article

Emerging hospitality markets confront a two-speed ecosystem where operational digitalization outpaces strategic AI readiness, creating a benefit–feasibility gap. Providers recognize substantial technology value yet face implementation constraints from costs, integration complexity, and skills shortages, while guests demonstrate acceptance conditional on trust, with privacy concerns suppressing willingness to pay. Drawing on dual-perspective empirical evidence from Albania’s accommodation sector consisting of a national provider readiness assessment (N = 1821) and a guest acceptance study (N = 689) conducted in prior research, this Design Science Research study develops a segment-differentiated technological blueprint through systematic integration of Design Thinking, service blueprinting, and systems thinking methodologies. Integrated TAM-TOE-DOI framework analysis reveals three distinct provider segments requiring differentiated implementation pathways: Tech Leaders positioned for AI capabilities, Selective Adopters benefiting from smart modules, and Skeptics requiring foundational capabilities. Empirical evidence establishes that regional ecosystem characteristics outweigh organizational scale in determining adoption feasibility, trust operates as a gating condition moderating acceptance and financial commitment, and supply–demand misalignment creates bottlenecks invisible to single-perspective assessments. Theoretical contributions extend TAM-TOE-DOI frameworks from explanatory constructs to design requirements, conceptualize supply–demand alignment as an adoption mechanism, and generate two generalizable design principles: dual-constraint satisfaction requiring simultaneous provider feasibility and guest acceptance, and trust-as-architecture embedding trust mechanisms as structural properties. The proposed segment-differentiated technological blueprint offers actionable implementation pathways aligned with varying levels of provider readiness, providing transferable guidance for policymakers, technology vendors, education providers, and accommodation providers across the Western Balkans, the Mediterranean, and other post-transition economies facing similar heterogeneity in readiness and resource constraints. Full article

Masonry retaining walls constitute an essential component of historic and urban infrastructure in seismic regions; however, their seismic performance remains insufficiently quantified due to material heterogeneity, limited tensile capacity, and complex soil–structure interaction. This study investigates the seismic response of historic stone masonry retaining walls using a finite element-based anisotropic macro-modeling approach. The analysis focuses on the perimeter retaining walls of Emirgan Grove in Istanbul, which represent culturally significant heritage structures constructed from natural limestone and cement–lime mortar. Material properties were defined based on experimental test results and representative values reported in the literature, while composite anisotropic behavior was incorporated into the numerical models. Static loads, earth pressures, and seismic actions were applied in accordance with the Turkish Building Earthquake Code (TBEC-2018) using the equivalent static earthquake load method. Representative wall segments with heights of 2.5 m, 3.5 m, 4.0 m, and 6.30 m were analyzed. The numerical results show that maximum compressive stresses reached approximately 0.48 MPa, remaining well below the allowable limit of 4.50 MPa, while maximum tensile stresses of about 0.28 MPa did not exceed the allowable tensile limit of 1.00 MPa. In contrast, shear stresses locally reached approximately 0.25 MPa, exceeding the allowable shear limit of 0.10 MPa, particularly along the soil–wall interface in taller walls. Sliding stability was satisfied in all cases, whereas overturning and shear behavior governed seismic vulnerability. These findings confirm that wall height is the primary parameter controlling seismic response and demonstrate the effectiveness of the proposed framework for preservation-oriented seismic safety assessment of historic masonry retaining walls. Full article

Autonomous surface vehicles can support water-quality monitoring, but they require planners that place measurements where they most improve the environmental estimate under mission constraints. This paper proposes ACO-Path, an informative path planner that couples Ant Colony Optimization -Ant System- with online Gaussian Process mapping. During the mission, the Gaussian Process updates a mean or contamination map and a variance or uncertainty map, from which dynamic action zones are derived and used to guide an explicit explore then exploit policy. The method is evaluated in a simulated water resource monitoring scenario inspired by Lake Ypacaraí, considering three exploration distances and two heuristic weights. In a comparison against five baseline planners, ACO-Path achieves the lowest hotspot error, $Erro{r}_{\mathrm{peak}}=0.19896\pm 0.39400$, while remaining competitive in global reconstruction, $MS{E}_{\mathrm{map}}=0.00144\pm 0.00348$, $R^2=0.96066\pm 0.09861$. In addition, a turning analysis based on the absolute heading change between consecutive segments $|\Delta \alpha |$ shows that ACO-Path produces smoother trajectories, with fewer sharp turns $|\Delta \alpha |\ge 45°$ than counterpart baselines under the same mission constraints. Full article

Using a quantile–frequency connectedness framework, this study analyzes the regime-contingent and horizon-specific transmission of shocks among AI assets, FinTech markets, and Blue Economy financial instruments. The empirical results reveal a distinctly asymmetric connectedness structure, whereby high-frequency spillovers intensify in upper-quantile states associated with liquidity stress and sentiment-driven trading, while low-frequency connectedness remains comparatively muted, thereby preserving cross-segment diversification potential. AI assets emerge as dominant net transmitters in short-horizon dynamics, reflecting rapid innovation cycles and speculative adjustments. FinTech markets exhibit stabilizing properties under median regimes but transition into net propagation roles when risk conditions escalate. Blue finance instruments act as conditional net absorbers, attenuating volatility originating from digital innovation-driven markets, particularly during adverse market states. By decomposing spillover intensities across quantiles and spectral bands, the analysis highlights a structural differentiation between innovation-sensitive digital assets and the comparatively stable behavior of blue-themed financial assets. These findings advance the understanding of nonlinear dependence, asymmetric contagion, and state-dependent co-movements in emerging financial ecosystems. The results provide actionable insights for systemic-risk measurement, cross-market shock diagnostics, and multi-asset portfolio construction in an increasingly interconnected global financial system. Full article