Search Results (388)

Cosmological filaments, galaxy clusters, and galaxies are magnetized reservoirs of cosmic rays (CRs). The exchange of CRs across these structures is usually modeled assuming that they remain charged and magnetically confined. At high energies, hadronic interactions can convert CR protons to neutrons. This physics is routinely included in air-shower and ultra-high-energy (UHE) CR propagation Monte Carlo simulations used for composition studies but is rarely treated explicitly in propagation models of CR transport and exchange between magnetized reservoirs. CR neutrons are not affected by magnetic fields and can propagate ballistically over kpc-Mpc distances before decaying back into protons, with relativistic time dilation extending their effective decay length. We show how such charged–neutral switching modifies CR confinement and escape in four representative environments: a Milky Way-like galaxy, a starburst galaxy, a galaxy cluster, and a cosmological filament. By solving the transport of a confined CR proton population in each structure using a diffusion/streaming propagation approach with hadronic pp and p$\gamma$ interactions, and treating neutron production and decay as a stochastic Poisson “jump” process, we find that neutron-mediated steps can allow additional CR escape from large-scale cosmological structures at energies where charged-particle transport alone would predict strong CR confinement and attenuation in ambient radiation fields. These effects imply a qualitative shift in how ultra-high-energy CRs are transferred from embedded sources into filaments and voids once intermediate neutron propagation is considered, with consequences for the partitioning of CRs across the large-scale structure of the Universe. Full article

Urban expansion is a key driver of land-use change and environmental pressure in rapidly urbanizing regions. Existing assessments of urban expansion often rely on predefined indicator systems and fixed weighting schemes, which limits their adaptability to evolving research priorities and regional contexts. This study develops an adaptive framework for urban expansion assessment by integrating a transformer-based language model with multi-source spatial data. A BERT-based semantic extraction process is used to identify relevant indicators and derive their relative weights from the scientific literature, enabling the construction of a literature-driven Urban Expansion Index (UEI). The framework is applied to the Central Plains Mega-city Region (CPMR), China, to examine spatial patterns and temporal dynamics of urban expansion between 2010 and 2020. Results show that UEI is primarily driven by land-use expansion indicators, while socioeconomic, infrastructure, and environmental indicators jointly reflect the multidimensional nature of expansion processes. Spatial patterns reveal a persistent concentration of high expansion intensity in core cities, alongside heterogeneous environmental responses and gradual outward growth. Changes in UEI display weaker spatial coherence than static levels, indicating differentiated local expansion dynamics. Local spatial autocorrelation analysis further identifies shifting clusters of urban expansion intensity, suggesting a reorganization of expansion centers within the agglomeration over time. By linking transformer-based indicator extraction with spatial analysis, this study advances urban expansion assessment beyond outcome-oriented mapping toward a more adaptive and knowledge-informed approach. The proposed framework is transferable to other mega-city regions and provides a useful tool for supporting territorial spatial planning and sustainable urban development. Full article

The stability of solar-dependent energy systems is vital for urban sustainability, but it is increasingly threatened by compound energy risks (CERs), events where low photovoltaic generation coincides with high electricity demand. This study addresses a critical knowledge gap by disentangling the co-evolving structural and magnitude drivers of these events to identify their propagation pathways and the most vulnerable districts. To achieve this, a novel hybrid framework was developed to provide a high-resolution, spatiotemporal assessment of both risk dimensions across Singapore’s 41 districts. Structural risk was mapped by integrating an undirected co-occurrence network, quantified using Mutual Information (MI), with a directed influence network derived from Bayesian Network Theory (BNT). Concurrently, magnitude risk was assessed through a copula-based analysis of joint probabilities for historical and future climate conditions, using Singapore’s new V3 dataset under multiple Shared Socioeconomic Pathways (SSPs). The results reveal a significant shift in the compound energy risk landscape. Structurally, the network of risk propagation evolves from a historically diffuse configuration to a consolidated system dominated by clusters of 8 to 9 highly interconnected districts under the SSP245 scenario. Under the high-diffusion SSP585 scenario, this evolution is expanded by the addition of 4 more districts. At the same time, the magnitude of risk intensifies across identified hotspot districts. This synthesis uncovers a critical feedback dynamic: districts such as 29, 36, and 40 not only serve as key structural hubs but also experience sharp increases in event probability, with their return periods for extreme compound events collapsing from over 50 years historically to the 10–20-year range. This forms a self-reinforcing loop of systemic vulnerability. These findings indicate that Singapore’s energy security will become increasingly exposed to climate-driven risks that propagate through this consolidated network, requiring targeted spatial adaptation to ensure long-term grid sustainability. Full article

On 12 July 2016, the ruling on the South China Sea Arbitration was announced and rapidly drew worldwide attention, turning the event into a major international hotspot. Quantifying the dynamics of such hotspot events and understanding the evolution of media-based inter-actor networks during major shocks are of substantial research interest. Viewing these interactions as dynamic networks, we analyze the time-varying actor interaction structure surrounding the arbitration using the Global Database of Events, Location and Tone (GDELT), a large-scale media-based event database with global coverage since 1979. We extract nearly 30,000 events related to the arbitration from 5 July to 25 July 2016, constructing daily cooperation and conflict networks to quantify structural changes via network size and degree-entropy dynamics. To further reveal actor-level structural roles, we learn node embeddings on each daily network via an entropy-driven graph representation learning scheme and perform embedding-based clustering with automatically selected cluster numbers, visualized via t-SNE. The results show that key dates in the event window are associated with pronounced structural shifts in the networks, including changes in participation breadth, degree-distribution heterogeneity, and clearer differentiation and reconfiguration of actor roles, with distinct patterns between cooperation and conflict networks. These findings demonstrate the potential of massive media event data for characterizing structural responses and actor-role evolution in event-driven inter-actor networks. Full article

Despite the well-documented acceleration of climate-related economic losses in Europe, existing research has largely treated these damages as isolated stochastic events rather than as structurally embedded fiscal risks. This gap leaves EU fiscal governance frameworks inadequately prepared for the persistent, spatially concentrated, and temporally dependent nature of such losses. This study addresses this gap by investigating the systemic transformation of climate-related economic risks within the European Union, arguing that climate losses have evolved from unpredictable stochastic shocks into a persistent, structural burden on the European economy. Adopting a comprehensive multi-methodological approach, the research quantifies this transition by integrating spatial concentration metrics (HHI), advanced time-series modelling (OLS, ARIMA, ETS), and anomaly detection techniques to analyse loss patterns across the EU-27 from 1980 to 2023. The empirical results demonstrate three critical systemic dimensions: (1) a statistically significant upward shift in the baseline of economic damages; (2) a high geographical concentration of losses, with Germany, Italy, and France consistently bearing the largest share of climate-driven fiscal pressure; and (3) the emergence of volatility clustering, indicating that climate risks are becoming increasingly non-linear and embedded in the macroeconomic environment. The study contributes to the literature by reframing climate-related economic losses as a systemic fiscal phenomenon requiring structural governance reform, rather than ad hoc disaster response. The findings suggest that existing reactive policy frameworks are insufficient to address the scale of these structural risks. Consequently, the paper advocates for a paradigm shift in EU climate policy—moving toward anticipatory fiscal instruments, harmonised resilience financing, and monitoring systems designed to mitigate systemic volatility and cross-country economic asymmetry rather than merely responding to isolated disaster events. Full article

Timber price dynamics are most often analysed using trends, seasonality, and classical measures of volatility, which describe the magnitude of fluctuations but only to a limited extent capture the temporal structure of the price-generating process. The aim of this study is to identify the structural complexity and long-term memory of quarterly prices of WC0 pine timber in the regional timber market in Poland. The analysis is based on nominal net prices (PLN/m³) from 16 forest districts of the Regional Directorate of State Forests in Kraków over the period 2005–2024, with reference to nationally averaged timber prices. Long-term dependence is assessed using the Hurst exponent estimated by detrended fluctuation analysis (DFA) applied to log returns, while the geometric complexity of price trajectories is characterised by the fractal dimension and additionally validated using the Higuchi estimator. Cross-sectional results reveal substantial spatial heterogeneity in scaling properties, indicating the coexistence of persistent (trend-following) and corrective (anti-persistent) dynamics across forest districts. Rolling-window analysis (40 quarters) demonstrates temporal variability in price dynamics, with particularly pronounced shifts observed in 2019–2021. Cluster analysis based on time-varying Hurst exponent values identifies two groups of forest districts with distinct persistence trajectories, corresponding to more trend-dominated and corrective price dynamics. In contrast, national-level prices generally exhibit higher persistence than local prices, reflecting the effects of price aggregation. Overall, the results show that fractal analysis uncovers persistent spatial and temporal differences in timber price structures that remain invisible when relying solely on variance-based measures, with direct implications for the choice of planning horizons and timber sale strategies in regional markets. Full article

Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive modality for brain-computer interfaces (BCIs), but robust signal interpretation is challenged by the significant temporal variability of the hemodynamic response. Standard linear methods, such as Pearson correlation, often fail to capture functional connectivity when signals exhibit temporal jitter. This study validates an unsupervised Dynamic Time Warping (DTW) clustering framework to robustly identify motor networks from fNIRS data by accommodating non-linear temporal shifts. We analyzed a public fNIRS dataset (N = 30) across right-hand (RHT), left-hand (LHT), and foot tapping (FT) tasks. A robust preprocessing pipeline was implemented, including Wavelet Motion Correction and Common Average Referencing (CAR) to remove artifacts and global systemic noise. The core method involved computing Z-score normalized DTW distance matrices, followed by hierarchical clustering. To validate the framework, we benchmarked it against a standard Pearson Correlation method. Results show that the unsupervised DTW framework achieved a network identification accuracy of 53.17%, significantly outperforming the standard Pearson correlation benchmark (48.06%) with a statistically significant difference (p < 0.05). The framework successfully detected distinct, somatotopically correct modulations: superior-medial activation during foot tapping and lateralized activation during hand tapping. These findings demonstrate that unsupervised DTW clustering is a robust, data-driven approach that outperforms conventional linear methods in capturing functional networks during motor tasks, showing significant potential for next-generation asynchronous BCIs. Full article

Background and Objectives: Burnout is highly prevalent among emergency and acute care healthcare workers (HCWs), yet biological correlates remain debated because candidate biomarkers are strongly shaped by circadian timing, shift work, sleep loss, and overlapping affective symptoms. We mapped post-2018 evidence of biological biomarkers assessed alongside validated burnout measures in emergency department (ED), emergency medical services (EMS), and related acute care settings. Specifically, we asked whether reproducible biological correlates of burnout can be identified in emergency and acute-care healthcare workers when biomarker endpoint class and sampling context are systematically considered. Materials and Methods: We conducted a systematic scoping review with evidence mapping (PRISMA-ScR). PubMed/MEDLINE and the MDPI platform were searched for English-language studies published from 2018 onward (through January 2026). Eligible quantitative studies enrolled ED/EMS or acute care HCWs, assessed burnout using validated instruments, and reported at least one biological biomarker. Evidence was charted by biomarker domain and endpoint class (basal measures, stress reactivity paradigms, and chronic indices such as hair-based markers). Results: Overall, 19 studies were included in mapping/synthesis. Biomarker selection clustered around the hypothalamic–pituitary–adrenal axis (cortisol; n = 10/19), with fewer studies focused on autonomic function (heart rate variability; n = 2/19) and immune–inflammatory markers (n = 2/19), and single-study coverage for oxidative stress (n = 1/19), cardiometabolic candidates (n = 1/19), cellular aging (n = 1/19), neuroglial/multi-system candidates (n = 1/19), and feasibility-oriented multi-marker designs (n = 1/19). Reported associations with burnout were heterogeneous in direction and magnitude, but were more interpretable when endpoint class, timing anchors, and shift/sleep-related covariates were explicitly reported. Rates of confounder adjustment were low across studies (e.g., only 3/19 reported multivariable adjustment, and none systematically measured sleep or circadian factors), substantially limiting interpretability. Conclusions: The 2018+ literature does not support a single reproducible biomarker for burnout in emergency and acute care workforces. Evidence instead suggests multi-system dysregulation that is highly sensitive to endpoint class, sampling timing, and contextual confounding. Future studies should prioritize timing-anchored repeated-measures protocols across shift and recovery windows, jointly model sleep/circadian factors and depressive symptoms, and evaluate multi-marker panels and intervention responsiveness. Full article

Against the backdrop of a gradual shift in the focus of cultural heritage (CH) conservation and utilization toward the integrated system formed by CH and its surrounding environment as well as regional systems, research on the coordinated protection of nature and culture to promote regional high-quality development has become a new trend. However, systematic summaries of the spatial–temporal distribution of CH in cross-regional typical geomorphic units at the river basin scale and their correlation with the natural environment remain insufficient. This study takes 387 Cultural Relics Protection Units in the Three Gorges of the Yangtze River (the Three Gorges region) as the research objects, utilizing GIS spatial analysis technology to examine the impact of the natural environment on CH across different periods and types. The theory of time-depth is introduced to reveal the layering mechanisms and underlying cultural logics. Coupled with the Minimum Cumulative Resistance (MCR) model, this study constructs a cultural corridor network and proposes spatial planning strategies. The findings are as follows: (1) The absolute core area for the distribution of CH across all periods remains the gentle slope zone near the river, characterized by elevations below 500 m, slopes within 25°, and distances from water systems within 1 km. However, the adaptive scope exhibits a diachronic evolution from core accumulation to peripheral expansion. (2) Different types of CH exhibited distinct natural adaptation strategies and vertical accumulation. Settlement Sites in the Before Qin Dynasty Period formed the foundational layer of survival rationality, while Ordinary Tombs in the Qin–Yuan Dynasty Period reinforced sedentism. Ancient Architecture in the Ming–Qing Dynasty Period underwent a transformation from “adapting to nature” to “reconstructing nature” as a product of environmental construction. Modern and Contemporary Significant Historical Sites and Representative Buildings in the After Qing Dynasty Period are characterized by a ruptured insertion on steep slopes, inscribing revolutionary memory onto space. The main stream of the Yangtze River serves as the core area of continuous deposition, while the extremely steep slopes form a distinctive stratigraphic accumulation of precipitous terrain. (3) Based on these distribution patterns, the study further proposes a spatial framework for CH called “One Corridor, Three Wings.” This framework uses the main stream of the Yangtze River as the spatial–temporal axis, linking the four core overlapping nodes of Fengjie, Wushan, Badong, and Xiling, supplemented by three secondary cultural clusters of the red heritage sites in southern Badong, the ancient town along the Daning River in Wushan, and the fortress sites in the Xiling–Yiling area. This research not only reveals the evolutionary path of CH in the Three Gorges region, but also provides a scientific basis for the systematic conservation and differentiated utilization of regional CH. Furthermore, it serves as a planning foundation and strategic reference for planning the Yangtze River National Cultural Park, as well as for the integrated preservation and utilization of river basin CH and linear CH with the aim of coordinated natural and cultural conservation. Full article

Global agricultural production is severely threatened by the intensification of crop diseases and pests. Traditional pesticide application methods, characterized by inefficiency and frequent phytotoxicity, necessitate the urgent development of smart plant protection technologies that feature precision, dosage reduction, and high efficiency. This study focuses on the core component of intelligent decision-making, systematically delineating the technological trajectory of the field through a three-tier analytical framework: “model evolution–system integration–application form.” Analysis reveals that decision-making models have transitioned from rule-driven and data-driven approaches to fusion-driven paradigms. This evolution marks a shift from the codification of empirical experience to data learning, culminating in the synergistic integration of multi-source information and domain knowledge. At the system application level, the core technical architecture—comprising multi-dimensional information sensing, real-time edge computing, and precise control execution—has facilitated the translation of intelligent pesticide application from laboratory settings to field deployment. Future decision-making systems are projected to evolve towards causal understanding, cluster collaboration, and ubiquitous service, providing critical technical support for the green transformation and sustainable development of agriculture. Full article

Climate change is advancing ripening and can impair phenolic maturity in grapes, compromising anthocyanins and stilbenes that affect the wine color and stability. We tested whether exogenous abscisic acid (ABA) mitigates warming-induced shifts in the phenylpropanoid pathway in the ’Malbec’ red wine grape variety. A factorial field experiment compared control temperature (−T) and elevated temperature (+T, +2.5 °C), with and without ABA sprays (three applications after veraison). Berry skin gene expression (ten flavonoid and stilbene genes) was monitored across ripening and summarized using time-course and AUC-based clustering. Anthocyanins were quantified in berry skins at harvest and in the corresponding wines, and stilbenes were quantified in wines. Warming reduced MYBA1 early in ripening and decreased anthocyanins and stilbenes overall. Meanwhile, ABA reinforced a late anthocyanin program under −T (MYBA1, UFGT, MYBC2-L3, F3′5′H), consistent with a shift toward the 3′,5′-hydroxylated/malvidin-type branch. Conversely, stilbenes remained suppressed under +T, with limited recovery under +T/+ABA. Time-integrated expression patterns and Spearman correlations consistently linked CHS2, F3′5′H, UFGT, MYBC2-L3, with variation in berry skin anthocyanins across treatments, while STS AUC tracked wine stilbenes. Overall, ABA partially buffered warming effects on ‘Malbec’ color by reinforcing late anthocyanin regulation but did not prevent warming-driven declines in wine stilbenes. Full article

Modeling nonstationary time series in financial and energy markets remains challenging due to nonlinear dynamics, volatility clustering, and frequent regime shifts that distort the underlying probabilistic structure of the data. This study introduces a novel probabilistic–statistical decomposition framework, termed Robust Adaptive Decomposition (RAD), designed to preserve probabilistic symmetry between deterministic and stochastic components. In this context, symmetry refers to maintaining statistical balance—particularly in the means, variances, and distributional structures—between the extracted modes and the residual series, thereby preventing artificial bias or variance distortion during decomposition. The RAD framework adaptively determines the optimal number of modes needed to effectively separate short-term fluctuations from long-term structural movements. Unlike conventional techniques, such as Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and CEEMDAN, the proposed method incorporates a robustness mechanism that mitigates mode mixing and reduces distortions induced by extreme shocks and regime transitions. The empirical evaluation is conducted on six oil-related energy commodities—Brent crude oil, kerosene, propane, sulfur diesel, heating oil, and gasoline—whose price dynamics exhibit pronounced nonlinearity and structural volatility. When integrated with ARIMA forecasting models, the RAD-based framework consistently outperforms benchmark decomposition approaches. Across all datasets, RAD–ARIMA achieves reductions of approximately 65–90% in MAE, 60–85% in RMSE, and up to 95% in MAPE relative to CEEMDAN-based models. These results demonstrate that RAD provides a mathematically rigorous and computationally efficient preprocessing mechanism that preserves statistical equilibrium while effectively disentangling deterministic structures from stochastic noise. Beyond oil markets, the framework offers broad applicability in econometric modeling, financial forecasting, and risk management, contributing to probability- and statistics-driven symmetry analysis in complex dynamic systems. Full article

The transport sector is among the largest final energy consumers and greenhouse gas (GHG) emitters in the European Union. Consequently, reducing energy-related externalities has become a central objective in the EU’s sustainability and decarbonisation policies. This study quantifies the disutility costs associated with energy consumption and emissions across major passenger transport modes—cars, buses, and trains—using a harmonised dataset encompassing 28 EU countries. To do so, a comprehensive disutility cost framework is established, integrating time losses, monetary costs, infrastructure requirements, noise, local air pollutants, and GHG emissions, and combining correlation, regression, and clustering analyses. The results indicate that car transport incurs the highest transport disutility costs, primarily due to congestion-related energy inefficiencies and GHG emissions. In contrast, rail transport demonstrates the lowest cost, energy- and emission-related disutilities across most EU countries. Bus transport represents an intermediate solution, providing lower emission intensity compared to cars but exhibiting higher energy-related disutilities than rail systems. The findings highlight that a modal shift toward rail- and bus-based transport systems can substantially reduce transport-related energy demand, emissions, and income expenses with transport cost at the EU level. While transport innovations and digitalisation may improve system efficiency, their benefits are unevenly distributed, suggesting that energy-focused transport policies should be complemented by measures to ensure inclusive access to low-emission mobility solutions. Full article

The paper presents a comprehensive methodological system for assessing the level of economic security of Russian regions, based on the synthesis of several complementary approaches and accounting for regional specifics. The central idea is a shift from static monitoring to dynamic analysis, which allows not only for capturing the current state but also for identifying the direction and stability of trends over time. The proposed methodology based on four stages: forming a set of indicators, normalizing their values, aggregating them into integral indices, and then visualizing them for operational decision-making. An important feature of sustainable development is the introduction of mechanisms to account for regional specifics through the clustering of regions and adjustment coefficients, which helps to mitigate the influence of geographical and structural differences on the results comparability. Together, they form an integrated system for diagnosing, planning, and monitoring the economic security of regions. The paper provides examples of threshold values for indicators such as the share of households with internet access, the length of the road network, birth rate, the volume of building commissioning, and innovation expenditures. A classification of regions into stability zones and recommendations for policy measures within each zone accompany the threshold analysis. In particular, for digitalization and transport infrastructure, measures are proposed to enhance monitoring, improve service accessibility, and invest in infrastructure; for the demographic component, measures are proposed to support families and improve quality of life. The practical significance of the research lies in creating a universal, yet flexible, toolkit for monitoring, ranking, and planning regional policy in the field of economic security. The proposed system was designed for application both at the federal level and for interregional analysis, including scenario planning and modeling the impact of management decisions. Thus, this study contributes to the literature by bridging the theory of economic security, the imperatives of sustainable regional development, and the practical potential of information technologies. It offers a concrete, scalable methodology for transforming regional economic security management into a data-driven, forward-looking, and context-sensitive process. In the future, the authors intend to further develop the methodology by considering the sectoral specialization of regions, integrating with medium- and long-term forecasting systems, and creating an automated monitoring platform. Full article

Canopy temperature (CT) is widely used to assess crop water and heat status, but it is often recorded at a single hour, implicitly treating CT as a stable trait. Here, we show that canopy cooling is a dynamic phenotype whose expression depends on time of day, phenological stage, and environment. First, we monitored 184 spring wheat (Triticum aestivum L.) genotypes in two Mediterranean environments (fully irrigated vs. rainfed, contrasting atmospheric demand) using UAV-based thermal imaging. CT was measured six times per day (10:30–17:30 h) at four reproductive stages (anthesis, milk-grain, milk-dough, and dough), enabling quantification of diurnal plasticity, seasonal shifts, and environmental effects on canopy cooling. Second, repeated-measures mixed models confirmed that Location, Stage, and Time of day, and all interactions, were highly significant (p < 0.001). Variance-component analyses showed a strong genetic signal within each Stage × Environment combination, with 87.6–97.7% of total variance attributable to genotypic effects pooled across hours. Third, the optimal phenotyping window was context dependent: under rainfed conditions, genotypic discrimination consistently peaked around mid-afternoon (~15:00 h), whereas under irrigation, the optimal window shifted with stage (13:30–15:00 h). Genotype rankings were also markedly less stable across hours under rainfed conditions, indicating substantial within-day re-ranking as atmospheric demand increased. Finally, thermal exposure analyses showed that exceeding a physiologically relevant threshold (CT > 32 °C) depended strongly on time of day and stage; maximum CT captured short heat events missed by daily means. Clustering and alluvial analyses revealed frequent reclassification across stages, with only a small subset remaining consistently cooler, particularly under stress. Random regression of CT on vapor pressure deficit (VPD) indicated that CT–VPD sensitivity was largely environment-dependent and showed weak cross-environment correspondence (Spearman ρ = −0.166). Overall, single-time-point CT phenotyping provides an incomplete view of thermal status, underscoring the need for multi-temporal protocols and context-specific measurement windows for breeding and physiological interpretation under drought and heat. Full article