Search Results (252)

Fully integrated airport access requires managing many aspects from both the passengers’ and the operational point of view. It is noted that air passenger preferences, influenced by distance, time, and other travel-related factors, are one of the fundamentals for understanding airport choice within multi-region airport systems. Therefore, an online survey was conducted in Europe, collecting more than two thousand responses, from which passengers’ attitudes and motives for selecting airport access travel modes were obtained. On the basis of the mobility profile of respondents, Fuzzy C-means (FCM) clustering analysis was performed to identify segments with similar travel attributes. The outcomes of clustering were validated through the comparison between the FCM and K-means clustering algorithms. The results of the study showed that (i) the car was the most preferred mode of transport across different age groups, and (ii) waiting time, travel costs, and travel time were rated as important, with reliability identified as the most important factor when making travel mode choices. These findings may serve as a reference for improving multimodal airport access services and encouraging a shift from private to public transportation modes. Full article

Coastal lagoons are highly vulnerable to human and climatic pressures, yet long-term ecological changes remain poorly quantified. We analyzed four decades (1980–2020) of data from fisheries from six lagoons in the Amvrakikos Gulf, Greece, using ecological indicators to assess trophic structure, exploitation status, and ecosystem responses. Cluster analysis of species level fishery production revealed a distinct temporal regime shift in the late 1990s–early 2000s, reflecting a major reorganization of species contributions to total yield. Mean total yield (Y), showed a consistent declining trend across lagoons, ranging from 2.7 ± 2.0 to 7.2 ± 5.0 t km⁻². Primary Production Required (PPR) declined (0.8–1.5 × 10¹⁰g C km⁻² yr⁻¹), while Mean Temperature of the Catch (MTC) increased in five lagoons (19.0–21.4 °C) and remained stable in one (20.0 ± 0.9 °C). Pelagic to demersal (P/D) ratios generally decreased (0.09–1.26), and Q-90 values were variable (0.8–2.2), highlighting site specific ecological dynamics. Short term yield predictions for 2021–2025 ranged from 0.78 to 6.75 t km⁻², remaining comparable to recent historical levels, while the estimated carrying capacities varied from 1.79 to 9.11 t km⁻², reflecting contrasting exploitation states among lagoons. These results demonstrate that multi-indicator, fishery-based analyses provide a robust framework for quantifying ecological change and guiding adaptive management in lagoon ecosystems. Full article

Payments for Ecosystem Services (PESs) have become a central instrument in global biodiversity finance, yet endangered species-specific PESs remain rare and poorly understood in implementation terms. Taiwan provides a revealing case: a three-year program paying farmers to conserve four threatened species—Prionailurus bengalensis, Lutra lutra, Tyto longimembris, and Hydrophasianus chirurgus—in working farmland across Taiwan and Kinmen island. Through semi-structured interviews with farmers, residents, and local conservation actors, we examine how payments are interpreted, rationalized, enacted, and emotionally experienced at the ground level. This study adopts Colaizzi’s data analysis method, the primary advantage of which lies in its ability to systematically transform fragmented and emotive interview narratives into a logically structured essential description. This is achieved through the rigorous extraction of significant statements and the subsequent synthesis of thematic clusters. Participants reported willingness to continue not only because subsidies offset losses, but because rarity, community pride, and the visible arc of “we helped this creature survive” became internalized rewards. NGOs amplified this shift by translating science into farm practice and “normalizing” coexistence. In practice, conservation work became a social project—identifying threats, altering routines, and defending habitat as a shared civic act. This study does not estimate treatment-effect size; instead, it delivers mechanistic insight at a live policy moment, as Taiwan expands PESs and the OECD pushes incentive reform. The finding is simple and strategically important: endangered-species PESs work best where payments trigger meaning—not where payments replace it. Full article

Introduction: Middle adolescence involves increasingly complex stressors, yet it remains unclear how coping strategies cluster into distinct profiles, how those profiles change across time, and whether profile structure is comparable across gender. We used latent class and transition analysis across three annual waves to identify coping profiles, model transitions, and examine perceived stress, depressive symptoms, and general self-efficacy by profile. Methods: Participants were 964 adolescents (mean age = 16.1 years; 56% female) from public high schools in Texas who completed surveys in spring 2011 with two annual follow-ups. The sample self-identified as Hispanic (32%), White (30%), African American (27%), or other (11%). Latent class/transition models estimated profile membership, transitions, and gender differences in prevalence and transition probabilities. Results: Four coping profiles emerged: Minimal Copers, Maximum Copers, Introverted Approach–Avoidant Copers, and Independent Problem-Solving Copers. Profile structure was comparable for females and males, although prevalence and transition differed. At Wave 4, Introverted Approach–Avoidant Copers reported the highest perceived stress and depressive symptoms, whereas Minimal and Independent Problem-Solving Copers reported lower perceived stress and depressive symptoms. Independent Problem-Solving and Maximum Copers reported higher general self-efficacy, whereas Minimal Copers reported the lowest. Conclusions: Coping in adolescence is heterogeneous and shifts over time, with gender differences in profile prevalence and transitions; findings highlight potential targets for tailored support and self-efficacy enhancement. Full article

As artificial intelligence systems proliferate across critical societal domains, understanding the nature, patterns, and evolution of AI-related harms has become essential for effective governance. Despite growing incident repositories, systematic computational analysis of AI incident discourse remains limited, with prior research constrained by small samples, single-method approaches, and absence of temporal analysis spanning major capability advances. This study addresses these gaps through a comprehensive multi-method text analysis of 3494 AI incident records from the OECD AI Policy Observatory, spanning January 2014 through October 2024. Six complementary analytical approaches were applied: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) topic modeling to discover thematic structures; K-Means and BERTopic clustering for pattern identification; VADER sentiment analysis for emotional framing assessment; and LIWC psycholinguistic profiling for cognitive and communicative dimension analysis. Cross-method comparison quantified categorization robustness across all four clustering and topic modeling approaches. Key findings reveal dramatic temporal shifts and systematic risk patterns. Incident reporting increased 4.6-fold following ChatGPT’s (5.2) November 2022 release (from 12.0 to 95.9 monthly incidents), accompanied by vocabulary transformation from embodied AI terminology (facial recognition, autonomous vehicles) toward generative AI discourse (ChatGPT, hallucination, jailbreak). Six robust thematic categories emerged consistently across methods: autonomous vehicles (84–89% cross-method alignment), facial recognition (66–68%), deepfakes, ChatGPT/generative AI, social media platforms, and algorithmic bias. Risk concentration is pronounced: 49.7% of incidents fall within two harm categories (system safety 29.1%, physical harms 20.6%); private sector actors account for 70.3%; and 48% occur in the United States. Sentiment analysis reveals physical safety incidents receive notably negative framing (autonomous vehicles: −0.077; child safety: −0.326), while policy and generative AI coverage trend positive (+0.586 to +0.633). These findings have direct governance implications. The thematic concentration supports sector-specific regulatory frameworks—mandatory audit trails for hiring algorithms, simulation testing for autonomous vehicles, transparency requirements for recommender systems, accuracy standards for facial recognition, and output labeling for generative AI. Cross-method validation demonstrates which incident categories are robust enough for standardized regulatory classification versus those requiring context-dependent treatment. The rapid emergence of generative AI incidents underscores the need for governance mechanisms responsive to capability advances within months rather than years. Full article

Objective: To analyze demographic traits, clinical complications, and healthcare use in patients with chronic liver disease across major etiologies in a large Romanian cohort. Methods: A retrospective study (2019–2023) of 2359 patients with chronic hepatitis C (CHC), hepatitis associated with alcohol (ALH), cirrhosis associated with alcohol (ALC), or non-alcoholic cirrhosis (NALC). Data on demographics, clinical outcomes, and hospitalizations were analyzed using descriptive statistics, regression modeling, and clustering in IBM SPSS 27.0.1. Results: CHC patients were oldest (mean 67.5 ± 12.3 years), while ALH patients were youngest (56.0 ± 11.0 years). CHC prevalence increased with age (10.0% in ≤30-year-olds to 87.1% in ≥81-year-olds; γ = 0.535, p < 0.001). Females comprised 60–70% of CHC cases, males > 85% of ALH and >78% of ALC. Mean hospitalization duration decreased from 13.80 days (2019) to 9.10 days (2023), yet cirrhotic patients had the longest stays (NALC: 16.37 ± 14.34; ALC: 17.66 ± 12.96) versus CHC (10.38 ± 10.14). Etiology was the strongest predictor of hospitalization length. Portal hypertension (PH) was the most common complication (54.3%), with males bearing more severe hepatic complications (ascites—38.3%; PH—66.8%). Conclusions: Hospital-based Romanian cohort analysis revealed that patient presentation and outcomes are fundamentally shaped by the interplay of etiology, sex, and age. We found a distinct female predominance in CHC, a pronounced male predominance in alcohol-related diseases, and evolving trends in non-alcoholic cirrhosis. These determinants dictate specific epidemiological patterns, hospitalization burdens, and complication risks, underscoring the critical need for a paradigm shift toward personalized, etiology-driven, and sex-tailored clinical management. Full article

Smart cities increasingly reuse existing Wi-Fi infrastructure to sense crowding, but many smart-campus tools still fail to support routine, day-to-day decisions. A short-horizon field feasibility study was conducted to prototype a low-maintenance, prefix-based early-warning rule that turns anonymized campus Wi-Fi access-point counts into an interpretable lunchtime crowd signal. Daily 7-min access-point profiles from five university canteens (11:00–14:00) were aggregated, winsorized, smoothed, and row-z-scored, then clustered into demand-shape typologies using k-means++. Two typologies were obtained (Early Peak and Late Shift), and a cosine-similarity atlas was frozen. At 11:28, the five-bin occupancy prefix was compared to typology centroids, and an Early Peak badge was issued when similarity to the Early Peak centroid exceeded a preset threshold. On held-out days, the Early Peak typology could be identified at 11:28 with coverage of 0.73 and agreement of 0.86 relative to end-of-day labels. In 20 matched canteen-weekday pairs, badge days were associated with a Hodges–Lehmann median reduction of 0.193 standard-deviation units in peak crowding (≈9% lower). Given the short (3-week) horizon and limited hold-out window, results are presented as feasibility evidence and motivate a larger controlled evaluation. Simple, interpretable rules built on existing Wi-Fi telemetry were shown to be deployable as a feasibility-level decision aid on a smart campus, while broader smart-city transferability should be validated through longer-horizon controlled evaluations. Full article

Early fruit development in tomato is driven by complex gene expression patterns and metabolic reprogramming, a crucial phase that shapes the fruit’s final size and structure. Previous studies using the Micro-Tom model have largely focused on later stages of development, especially ripening, leaving early developmental processes relatively unexplored. To address this knowledge gap, we performed RNA-seq analyses on Micro-Tom fruits harvested at three key developmental stages: 3, 5, and 8 days post-anthesis (DPA). Pairwise differential gene expression analyses revealed that the most extensive transcriptional reprogramming occurs during the transition from 5 to 8 DPA, while comparatively fewer changes were observed between 3 and 5 DPA. K-means clustering of 11,035 stably expressed genes revealed nine distinct expression profiles associated with specific developmental phases, including cell proliferation, transition, and cell expansion. Integrating transcriptomic and metabolomic datasets uncovered coordinated shifts in gene expression and metabolite accumulation, highlighting both conserved regulatory mechanisms and cultivar-specific pathways governing early fruit development. These findings advance our understanding of the molecular regulation of early fruit development in Micro-Tom tomatoes and provide a basis for future efforts to improve fruit quality and yield. Full article

The phytoplankton community structure is regulated by environmental conditions, influencing ecosystem stability and productivity. In August 2023, a survey was conducted at 28 stations in the Yellow River Estuary (YRE) and adjacent coastal waters, where phytoplankton communities, nutrients, chlorophyll-a, and other environmental factors were synchronously analyzed. Across-site comparison, redundancy analysis (RDA), and K-means clustering were applied to characterize spatial patterns and identify key factors controlling diatom to dinoflagellate ratios and dominant taxa. The nutrient structure, particularly DIN/PO₄³⁻, corresponded closely with the spatial shift between diatom and dinoflagellate dominance. Offshore areas dominated by diatoms (Cerataulina, Chaetoceros) exhibited higher salinity and more balanced nutrient ratios, whereas nearshore zones influenced by Yellow River inputs had high DIN, low PO₄³⁻, and evident phosphorus limitation, favoring dinoflagellates (Noctiluca, Heterodinium). These results indicate that nutrient imbalance and salinity gradients are likely the main drivers of diatom-to-dinoflagellate transitions and shape the phytoplankton composition in the estuary coastal waters. This study provides insights linking nutrient imbalance to phytoplankton community succession and advances the understanding of estuarine phytoplankton dynamics. Full article

Complex odor emissions from wastewater treatment plants consist of multiple volatile compounds that exhibit heterogeneous temporal dynamics and low linear correlations, making accurate prediction and interpretation difficult when analyzed on a single-compound basis. This study investigates whether clustering can serve not only as an exploratory tool but as an essential preprocessing step to enhance machine-learning performance in multi-odor prediction systems. A total of 22 designated odorants were continuously monitored, and their pairwise dependencies were evaluated using Pearson correlation and mutual information. Data-driven clustering was performed through K-means, hierarchical linkage, and principal-component–based latent grouping, and the resulting structures were quantitatively compared with functional-group-based chemical classifications using the consistency ratio and Jaccard similarity index. Cluster validity was further examined using the Silhouette Coefficient, Davies–Bouldin Index, and Calinski–Harabasz Index. The predictive contribution of clustering was verified by training XGBoost regression models on both raw and cluster-structured datasets. The clustered dataset yielded higher predictive accuracy, with increased R² and reduced MAE and RMSE across most odorants. SHAP analysis further confirmed that clustering improved model interpretability by stabilizing feature contributions and reducing noise-driven importance shifts. The findings demonstrate that clustering is not a supplementary diagnostic tool, but a prerequisite for building reliable, high-performance machine-learning models in complex odor systems. This integrative framework offers a methodological foundation for multi-odor forecasting, source tracking, and next-generation odor management platforms. Full article

The Metaponto coastal plain (Ionian margin, Southern Italy) records the Late Pleistocene–Holocene evolution of a foredeep coastal system shaped by relative sea-level change, vertical land motion, and compaction. We analyze a 22 m continuous core (Meta 1) using lithofacies logging, grain size statistics and cumulative curves, multivariate analysis of grain size distributions (PCA and k-means clustering), and three AMS 14C ages, and we compare the record with a nearby borehole (MSB) and a global eustatic curve. Four depositional units document a shift from lower-shoreface–offshore deposition to lagoon–barrier/aeolian systems, culminating in late Holocene near-surface progradation. Textural end members (mud-rich offshore/lagoonal, traction-dominated, and sand-rich) are coherent across classical parameters, Visher-type curves, PCA, and k-means clusters. Depth–age comparisons suggest net uplift during the Late Glacial, followed by near-present relative sea level and a Late Holocene onset of modest net subsidence; a compaction contribution is plausible but unquantified. Subsidence/uplift rates therefore remain upper-bound estimates owing to sparse chronological control and the lack of glacio-isostatic and compaction modeling. Together with the MSB emerged-beach tie-point, the record constrains shoreline position and progradation. The inferred Mid- to Late-Holocene stabilization and progradational trends are consistent with other Italian and wider Mediterranean coastal plains. Additional dating and quantitative paleoecological proxies (e.g., foraminifera/ostracods/molluscs) are key to independently constrain salinity and water-depth changes and to refine the partitioning between subsidence and compaction. Full article

Against the backdrop of intensifying global climate change and human activities, the increasing frequency and evolution of major wildfire events pose severe challenges to global disaster prevention and mitigation systems. Systematically understanding their disaster characteristics, spatiotemporal patterns, and societal response efficacy is an urgent scientific requirement for formulating effective coping strategies. This study constructed a comprehensive database covering 137 major global wildfire events from 2018 to 2024, with data sourced from GFED, EM-DAT, and official national reports. Utilizing a synthesis of methods including descriptive statistics, spatiotemporal clustering analysis, K-means pattern recognition, and non-parametric tests, a multi-dimensional quantitative analysis was conducted on disaster characteristics, evolutionary trends, casualty patterns, and policy effectiveness. Despite potential reporting biases and heterogeneous data standards across countries, the analysis reveals the following: (1) All key wildfire metrics (e.g., burned area, casualties, evacuation scale) exhibited extreme right-skewed distributions, indicating that a minority of catastrophic events dominate the overall risk profile; (2) Global wildfire hotspots demonstrated dynamic expansion, spreading from traditional regions in North America and Australia to emerging areas such as Mediterranean Europe, Chile, and the Russian Far East, forming three significant spatiotemporal clusters; (3) Four distinct casualty patterns were identified: “High-Lethality”, “Large-Scale Evacuation”, “Routine-Control”, and “Ecological-Destruction”, revealing the differentiated formation mechanisms under various disaster scenarios; (4) A substantial gap of nearly 65 times in emergency evacuation efficiency—defined as the ratio of evacuated individuals to total casualties—was observed between developed and developing countries, highlighting a significant “development gap” in emergency management capabilities. This study finds evidence of increasing extremization, expansion, and polarization in global wildfire risk within the 2018–2024 event sample. The conclusions emphasize that future risk management must shift from addressing “normal” events to prioritizing preparedness for “catastrophic” scenarios and adopt refined strategies based on casualty patterns. Simultaneously, the international community needs to focus on bridging the emergency response capability gap between nations to collectively build a more resilient global wildfire governance system. Full article

Monitoring land use and land cover (LULC) transformations is essential for understanding socio-ecological dynamics. This study assesses structural shifts in Romania’s landscapes between 1990 and 2018 by integrating algorithmic complexity, fractal analysis, and Grey-Level Co-occurrence Matrix (GLCM) texture analysis. Multi-year maps were used to compute Kolmogorov complexity, fractal measures, and 15 GLCM metrics. The measures were compiled into a unified matrix, and temporal trajectories were explored with principal component analysis and k-means clustering to identify inflection points. Informational complexity and Higuchi 2D decline over time, while homogeneity and angular second moment rise, indicating greater local uniformity. A structural transition around 2006 separates an early heterogeneous regime from a more ordered state; 2012 appears as a turning point when several indices reach extreme values. Strong correlations between fractal and texture measures imply that geometric and radiometric complexity co-evolve, whereas large-scale fractal dimensions remain nearly stable. The multi-index approach provides a replicable framework for identifying critical transitions in LULC. It can support landscape monitoring, and future work should integrate finer temporal data and socio-economic drivers. Full article

Lapis lazuli is a valued gemstone that displays a wide spectrum of blue hues, yet the quantitative link between its color and internal sulfur speciation remains unresolved. This study integrates colorimetry with electron probe microanalysis and UV-Vis, Raman, and X-ray photoelectron spectroscopy to establish this relationship and build a robust grading framework within the CIE 1976 L*a*b* color space. X-ray diffraction was employed to determine the mineral composition and confirm that the chromogenic elements originated from lazurite. K-means clustering with Fisher’s discriminant validation classifies samples into four grades: Fancy Blue, Fancy Intense Blue, Fancy Deep Blue, and Fancy Dark Blue. Multimodal analyses identify three sulfur species—[S₃]^·−, S²⁻, and ${\mathrm{SO}}_4^{2-}$—and show that higher sulfur content correlates with lower lightness, reduced chroma, and a violetish-blue shift. [S₃]^·− is confirmed as the dominant chromophore, producing the strong 600 nm absorption that defines the blue hue. A weak absorption band observed near 400 nm in some samples can be attributed to S²⁻ and ${\mathrm{SO}}_4^{2-}$ species, but no visually perceptible effect of this band on the overall color was detected. Full article

5G technology represents a transformative shift in mobile communications, delivering improved ultra-low latency, data throughput, and the capacity to support huge device connectivity, surpassing the capabilities of LTE systems. As global telecommunication operators shift toward widespread 5G implementation, ensuring optimal network performance and intelligent resource management has become increasingly obvious. To address these challenges, this study explored the role of advanced clustering methods in optimizing cellular networks under heterogeneous and dynamic conditions. A systematic literature review (SLR) was conducted by analyzing 40 peer-reviewed and non-peer-reviewed studies selected from an initial collection of 500 papers retrieved from the Semantic Scholar Open Research Corpus. This review examines a diversity of clustering approaches, including spectral clustering with Bayesian non-parametric models and K-means, density-based clustering such as DBSCAN, and deep representation-based methods like Differential Evolution Memetic Clustering (DEMC) and Domain Adaptive Neighborhood Clustering via Entropy Optimization (DANCE). Key performance outcomes reported across studies include anomaly detection accuracy of up to 98.8%, delivery rate improvements of up to 89.4%, and handover prediction accuracy improvements of approximately 43%, particularly when clustering techniques are combined with machine learning models. In addition to summarizing their effectiveness, this review highlights methodological trends in clustering parameters, mechanisms, experimental setups, and quality metrics. The findings suggest that advanced clustering models play a crucial role in intelligent spectrum sensing, adaptive mobility management, and efficient resource allocation, thereby contributing meaningfully to the development of intelligent 5G/6G mobile network infrastructures. Full article