Search Results (56)

Children living in agricultural regions are exposed to pesticides through multiple environmental and occupational exposure processes; however, the relative contribution of these processes remains insufficiently characterised in many rural contexts of the Global South. This study assessed pesticide exposure among children residing in an agricultural community in western Mexico characterised by close spatial proximity between residential areas and intensively cultivated fields. Urine samples were collected from children at two points in the agricultural cycle (March and December 2018). Pesticide concentrations were determined using liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). Paired longitudinal analyses were conducted to evaluate intra-individual changes in detection frequencies and urinary concentrations across sampling periods. Multiple pesticides were detected, including compounds with near-universal presence across both sampling periods. Significant increases in urinary concentrations were observed between March and December for several pesticides, consistent with seasonal agricultural dynamics, while no systematic differences were identified between children from agricultural and non-agricultural households. These findings indicate that pesticide exposure in this setting operates as a community-level exposure regime that is both structurally produced and territorially embedded. Exposure patterns reflect the convergence of agricultural practices, environmental dispersion processes, and spatial configurations that extend beyond occupational boundaries. The results highlight the limitations of risk models focused exclusively on individual or occupational exposure and underscore the need for public health strategies that address pesticide exposure as a structurally produced and territorially embedded condition. Full article

Cropland protection enforcement is central to food security and sustainable land management, yet small-scale encroachments within Permanent Basic Farmland (PBF) boundaries frequently evade conventional field surveys and reactive inspection regimes. Existing remote sensing approaches rely mainly on comprehensive land-cover classification or bi-temporal change detection, which often generate alerts beyond the regulatory scope and require annotation efforts that limit county-scale deployment. To address this gap, this study reframes PBF monitoring as a boundary-constrained anomaly screening task, defined as the detection of surface conditions that deviate from expected cultivation norms within legally defined parcels. To operationalise this task, we adapt a DeepLabv3+-based segmentation pipeline by incorporating an auxiliary edge branch and a composite loss to improve sensitivity to minority-class anomalies and preserve fragmented parcel boundaries. The model is trained on the LoveDA dataset and evaluated in Mancheng District, Hebei Province, China, without site-specific fine-tuning. Multi-temporal imagery from 2021 to 2023 is further used as a post hoc consistency check to distinguish persistent anomalies from transient surface conditions, rather than to model temporal dynamics explicitly. Cross-regional zero-shot evaluation further examines model robustness under heterogeneous environmental conditions. Benchmarked against five comparison architectures, the adapted pipeline achieves a Recall of 61.25%, representing a 10.24 percentage-point improvement over DeepLabv3+ and expanding the set of candidate encroachments for field verification. This result should be interpreted in terms of screening sensitivity rather than overall segmentation optimisation. The outputs are intended as preliminary screening leads that support, rather than replace, expert review. The principal contribution of this study therefore lies in reframing PBF monitoring as an operational anomaly-screening task aligned with enforcement needs, rather than in proposing a fundamentally new segmentation architecture. Full article

History attempts to make sense of disparate information by trying to create discourse that lays a series of events with crisp cause–effect relationships in a sequence. Epochal shifts, such as the change from Antiquity to the Middle Ages, are especially complex since they involve a large number of economic, political and even religious factors which occur over long periods and that might overlap and interact through reciprocal feedback mechanisms, making this cause–effects sequence difficult to establish. In this research we adopt a data-driven and well-established methodology to identify, with quantifiable statistical precision, the moment when this shift happened, and from there arrive at its possible causes. We will use historical coin hoard data to find out whether such a shift is detected in a peripheral part of the Roman Empire, the Iberian Peninsula. To do so, we will apply different changepoint analysis methods to a time series of trade links created from that data, and conduct a retrospective analysis based on that result, analyzing the structure of the trade networks before and after the link. Thus, we progress from identifying when the shift happened to identifying where it took place, which in turn allows us to get to investigate why it happened, namely, historical events that could have caused it. This methodology can be used to analyze epochal changes in several steps using time-stamped network data, possibly finding disregarded causes or cause–effect links that could have been overlooked by qualitative methods; in this case, we have applied it to a dataset of coin hoards either found in the Iberian Peninsula or including coins minted there, finding a changepoint in the early 5th century, which, through network analysis, has been linked to a loss of trade with the area of Britannia. Full article

The mobile industry has experienced long-run changes in its knowledge structure, including identifiable transition points observable through embedding-based semantic analysis. Using abstracts from 86,674 mobile industry publications published between 2005 and 2024, we embed documents with SPECTER2, build year-specific embedding distributions, and derive knowledge regimes by combining change-point detection with inter-year distribution distances. We then extract regime-specific topics via clustering and reconstruct topic lineages by aligning topic similarities to classify inheritance, differentiation, convergence, and disappearance. The analysis delineates three regimes spanning 2005 to 2012, 2013 to 2019, and 2020 to 2024, with pronounced transitions around 2012 to 2013 and 2019 to 2020. Regime 1 centers on foundational technologies such as wireless communication, power, sensors, and reliability. Regime 2 expands toward platforms, apps, and data analytics alongside cross-domain convergence. Regime 3 is characterized by strengthened 5G operations and data-driven services, together with the independent rise in policy, governance, and regulation topics. Transitions reflect recombination built on inherited knowledge rather than abrupt replacement, and post-transition topics display distinct growth typologies by network position and growth pattern. By integrating embedding-based changepoint detection with topic lineage reconstruction, we provide a reproducible account of regime transitions and quantitative evidence to inform the timing of corporate R&D, standard and platform strategies, and policy and regulatory design. Full article

The Virial theorem has been applied with considerable success in various fields of natural sciences. This work proposes an extension of the theorem applied to discrete data series. This application will be called the Virial theorem extension and can be applied to the numerical solution of nonlinear dynamic systems represented by difference equations, such as logistic, discubic and random number generators, the numerical solution of differential equations like the nonlinear double pendulum and a series of pseudorandom numbers and its reciprocals. For this purpose, a coefficient was derived from the discrete Virial formalism. This coefficient can be used to detect when a time series is obtained as the solution of a differential equation, in which case the coefficient is close to 1, and when the data come from other sources, in which case it takes different values. With reference to chaotic dynamic systems, the discrete Virial coefficient shows the feasibility in the detection of a change in behavior, as an alternative to the traditional calculation of Lyapunov exponents, and it is a thousand times faster. The convergence speed of the final value of the discrete Virial coefficient of a dynamic system in a non-chaotic regime is between one and five orders of magnitude greater than in the chaotic regime, thus extending results in non-Hamiltonian systems, previously found by another author in Hamiltonian systems. The results obtained show that the proposal characterizes and distinguishes different types of behavior from the series under study. It also shows great sensitivity to the evolution of the series, even anticipating critical points. The proposed method to construct the discrete Virial extension does not require the existence of a Hamiltonian, which allows its application to a series obtained experimentally or from any differential equation. From a general point of view, this research shows a series of properties that can be reinterpreted in light of the discrete Virial coefficient, providing a novel and versatile tool, given its minimal applicability requirements. For pseudorandom number series, the extension reveals a consistent, quasi-mirror behavior between its kinetic and potential factors, suggesting an underlying structural property. Full article

As key headwater regions of the upper Yangtze River, the Yalong and Dadu River basins are expected to experience highly uncertain hydrological responses under climate warming. However, the nonlinear and spatially heterogeneous evolution of streamflow across multiple time-frequency scales remains insufficiently understood. In this study, a SWAT model driven by CMIP6 climate projections under four shared socioeconomic pathways (SSP1-2.6 to SSP5-8.5) was coupled with multivariate wavelet coherence, spatial wavelet transform, and change-point detection methods to investigate the spatiotemporal evolution of streamflow and extreme risks during 2017–2100. Results indicate that precipitation is the primary driver of streamflow variability, with streamflow responding rapidly, while air temperature mainly regulates seasonal intensity via snowmelt. Streamflow seasonal intensity exhibits a northwest-southeast gradient, with low variability upstream and high sensitivity downstream, reflecting precipitation-concentrated, forested canyons where rapid lateral flow and dry-season evapotranspiration amplify flow contrasts. Moreover, hydrological nonstationarity and extreme risks are projected to intensify, with structural regime shifts emerging in the 2040s–2050s and extreme high-flow magnitudes doubling under SSP5-8.5, accompanied by more frequent drought-flood alternations. These findings highlight an upstream buffering-downstream sensitivity pattern, emphasizing the need for spatially differentiated water resources management under nonstationary climate conditions. Full article

Potential evapotranspiration (PET) and water balance (WB) are key indicators of hydroclimatic conditions and water availability, particularly in arid and semi-arid regions. This study investigates the interannual variability, long-term trends, and abrupt regime shifts in annual PET and WB across Saudi Arabia using multi-station observational data spanning 1985–2022. PET was estimated using a temperature-based approach suitable for data-scarce arid environments, and WB was calculated as the difference between precipitation and PET. Non-parametric statistical methods were applied to assess trend magnitude and significance, while Pettitt’s change-point test was used to identify abrupt shifts at both regional and station scales. The main findings show a widespread and spatially coherent increase in atmospheric evaporative demand, with predominantly positive PET trends at both regional and station scales, accompanied by persistently negative and increasingly declining WB values, indicating a long-term intensification of water deficit across much of the country. Spatial patterns of PET and WB closely follow gradients in energy availability and temperature, confirming the dominant influence of warming-driven processes on hydroclimatic conditions in this arid environment. Change-point analysis identifies a statistically significant regional hydroclimatic regime shift during the late 1990s, characterized by an abrupt increase in PET and a concurrent deterioration of WB, marking the onset of a more water-limited climatic regime. At the station scale, the timing and significance of detected change points display pronounced spatial heterogeneity, reflecting the modulation of regional climatic forcing by local climatic and geographic factors. Overall, the results demonstrate that increasing evaporative demand, rather than precipitation variability alone, has become a primary control on water availability across Saudi Arabia, highlighting the importance of explicitly accounting for hydroclimatic non-stationarity in water resource assessment and long-term planning under continued warming conditions. Full article

Coral reefs provide immense ecosystem and economic value, supporting biodiversity, fisheries, tourism, and coastal protection worth billions annually. However, widespread degradation from thermal stress, storms, disease, and human impacts has caused significant coral cover and reef structure loss, increasing coastal vulnerability and economic risks. While coral loss is well-documented, degradation of underlying reef infrastructure and surrounding seafloor changes remain poorly understood. This study addresses this knowledge gap by quantifying seafloor elevation and volume changes across 234.2 km² of the Upper Florida Keys (UFK) reef tract using historical bathymetric and modern lidar (light detection and ranging) data collected from two periods with distinctly different disturbance regimes: 1935–2002 (frequent storms and major coral loss) and 2002–2016 (few storms and persistently low coral cover). Analysis of over 25,000 data points revealed substantial elevation and volume loss during 1935–2002 (−0.1 ± 0.8 m; 13.6 × 10⁶ m³ net loss), shifting to minimal gains by 2002–2016 (0.0 ± 0.3 m; 1.6 × 10⁶ m³ net gain). Despite this shift, benthic cover data showed continued declines in stony coral, with increases in macroalgae and octocorals, indicating that limited reef accretion persists even with reduced storm activity. Spatial analyses highlighted variable accretion and erosion patterns across habitats and subregions, underscoring the limitations of localized measurements for ecosystem-wide assessments. Our findings demonstrate the value of integrating historical and modern datasets for regional reef monitoring, establishing baselines for restoration planning, and emphasizing the need for continued high-resolution monitoring to guide adaptive management amid ongoing environmental change. Full article

This study investigates the structural evolution and projected trajectory of greenhouse gas (GHG) emissions across the EU27 from 1990 to 2030, with a particular focus on their implications for the effectiveness of European climate policy. Drawing on official sectoral data and employing a multi-method framework combining time series modelling (ARIMA), machine learning (Random Forest), regime-switching analysis, and segmented linear regression, we assess past dynamics, detect structural shifts, and forecast future trends. Empirical findings, based on Markov-switching models and segmented regression analysis, indicate a statistically significant regime change around 2014, marking a transition to a new emissions pattern characterised by a deceleration in reduction rates. While the energy sector experienced the most significant decline, agriculture and industry have gained relative prominence, underscoring their growing strategic importance as targets for policy interventions. Hybrid ARIMA–ML forecasts indicate that, under current trajectories, the EU is unlikely to meet its 2030 Fit for 55 targets without adaptive and sector-specific interventions, with a projected shortfall of 12–15 percentage points relative to 1990 levels, excluding LULUCF. The results underscore critical weaknesses in the EU’s climate policy architecture and reveal a clear need for transformative recalibration. Without accelerated action and strengthened governance mechanisms, the post-2014 regime risks entrenching a plateau in emissions reductions, jeopardising long-term climate objectives. Full article

Long-term wind speed changes over the Yangtze River waterway have critical implications for inland shipping efficiency, emission dispersion, and renewable energy potential. This study utilizes a high-resolution 5 km gridded reanalysis dataset spanning 1979–2018 to conduct a comprehensive spatiotemporal analysis of surface wind climatology, variability, and trends along China’s primary inland waterway. A pivotal regime shift was identified around 2000, marking a transition from terrestrial stilling to a recovery phase characterized by wind speed intensification. Multiple change-point detection algorithms consistently identify 2000 as a pivotal turning point, marking a transition from the late 20th century “terrestrial stilling” to a recovery phase characterized by wind speed intensification. Post-2000 trends reveal pronounced spatial heterogeneity: the upstream section exhibits sustained strengthening (+0.02 m/s per decade, p = 0.03), the midstream shows weak or non-significant trends with localized afternoon stilling in complex terrain (−0.08 m/s per decade), while the downstream coastal zone demonstrates robust intensification exceeding +0.10 m/s per decade during spring–autumn daytime hours. Three distinct wind regimes emerge along the 3000 km corridor: a high-energy maritime-influenced downstream sector (annual means > 3.9 m/s, diurnal peaks > 6.0 m/s) dominated by sea breeze circulation, a transitional midstream zone (2.3–2.7 m/s) exhibiting bimodal spatial structure and unique summer-afternoon thermal enhancement, and a topographically suppressed upstream region (<2.0 m/s) punctuated by pronounced channeling effects through the Three Gorges constriction. Critically, the observed recovery contradicts widespread basin greening (97.9% of points showing significant positive NDVI trends), which theoretically should enhance surface roughness and suppress wind speeds. Correlation analysis reveals that wind variability is systematically controlled by large-scale atmospheric circulation patterns, including the Northern Hemisphere Polar Vortex (r ≈ 0.35), Western Pacific Subtropical High (r ≈ 0.38), and East Asian monsoon systems (r > 0.60), with distinct seasonal phase-locking between baroclinic spring dynamics and monsoon-thermal summer forcing. These findings establish a comprehensive, fine-scale climatological baseline essential for optimizing pollutant dispersion modeling, and evaluating wind-assisted propulsion feasibility to support shipping decarbonization goals along the Yangtze Waterway. Full article

Rainfall and streamflow analyses have long been central to hydrological research, yet traditional approaches often overlook the complexity introduced by changing climate signals, land-use dynamics, and human infrastructure. This study applies an integrated, data-driven framework to explore emerging hydroclimatic shifts in the Navasota River Watershed of east-central Texas. By combining autocorrelation analysis, Mann–Kendall and modified Mann–Kendall trend tests, and Pettitt’s change-point detection, we examine more than a century of precipitation and streamflow records alongside post-1978 reservoir operations. Results reveal an accelerating wetting tendency, particularly evident in decadal rolling averages and early-summer precipitation, accompanied by a statistically significant increase in 10-year moving averages of annual peak streamflow. While abrupt regime shifts were not detected, subtle but persistent changes point to evolving watershed memory and heightened flood risk in the post-dam era. This study reframes rainfall and streamflow trend analysis as a dynamic tool for anticipating hydrologic regime shifts, highlighting the urgent need for adaptive water infrastructure and flood management strategies in rapidly urbanizing and climate-sensitive watersheds. Full article

These days, the prices of fruits and vegetables fluctuate significantly, causing issues in the supply chain and for perishable products. This study aimed to use hybrid machine learning methods to cluster regional Chilean produce from 2015 to 2023 based on market analysis and address fluctuations in price and demand for agricultural products. The hybrid model employed in this research included substantial noise reduction with interquartile range (IQR), Z-score, and Hampel filters; temporal-spectral feature extraction through additive decomposition and Fast Fourier Transform (FFT); principal component analysis (PCA) for reducing dimensions; Gaussian mixture models (GMMs) for probabilistic clustering; and regime-shift detection using cumulative sum (CUSUM) and Bayesian online change-point detection (BOCPD). Finally, a sensitivity analysis demonstrated the approach’s reliability and robustness. The novelty of this research lies in the introduction of a hybrid model for forecasting agricultural product prices across Chile’s various regions. Full article

Wildfire regimes in Mediterranean landscapes are becoming increasingly unpredictable, driven by the combined effects of climate change, land-use transitions, and socio-economic pressures. Traditional metrics such as burned area or ignition points often fail to capture the complexity of the temporal and spatial recurrence of fire events. To address this gap, we apply, for the first time, a trajectory analysis framework to wildfire occurrence data across mainland Portugal (1975–2024), using pixel-level binary time series at 100 m resolution. Originally developed for land cover change detection, this method classifies each pixel into sequences representing distinct temporal patterns (e.g., stability, gains, losses, or alternations) over defined periods. Results reveal a predominance of stable absence and alternation-type trajectories, particularly “All alternation gain first”, which points to recurrent yet irregular fire activity. Regional differences further highlight the influence of divergent socio-ecological contexts. The findings suggest that fire regimes in Portugal are not only recurrent but structurally dynamic, and that trajectory-based classification offers a novel and valuable tool for long-term monitoring and regionally adapted fire management. Applying this method to wildfire data required specific adjustments to account for the unique temporal and thematic characteristics of fire regimes, ensuring a meaningful interpretation of the results. Full article

This paper develops a dynamic information-theoretic network framework to quantify systemic risk in China’s maritime–commodity nexus with a focus on the Yangtze River Basin using eight monthly indicators, CCFI, CBCFI, BDI, YRCFI, GAUP, MPCT, CPUS, and ASMC. We resample, impute, standardize, and difference series to achieve stationary time series. Nonlinear interdependencies are estimated via KSG mutual information (MI) within sliding windows; networks are filtered using the Planar Maximally Filtered Graph (PMFG) with bootstrap edge validation (95th percentile) and benchmarked against the MST. Average MI indicates moderate yet heterogeneous dependence (about 0.13–0.17), revealing a container/port core (CCFI–YRCFI–MPCT), a bulk/energy spine (BDI–CPUS), and commodity bridges via GAUP. Dynamic PMFG metrics show a generally resilient but episodically vulnerable structure: density and compactness decline in turbulence. Stress tests demonstrate high redundancy to diffuse link failures (connectivity largely intact until ∼70–80% edge removal) but pronounced sensitivity of diffusion capacity to targeted multi-node outages. Early-warning indicators based on entropy rate and percolation threshold Z-scores flag recurring windows of elevated fragility; change point detection evaluation of both metrics isolates clustered regime shifts (2015–2016, 2018–2019, 2021–2022, and late 2023–2024). A Systemic Importance Index (SII) combining average centrality and removal impact ranks MPCT and CCFI as most critical, followed by BDI, with GAUP/CPUS mid-peripheral and ASMC peripheral. The findings imply that safeguarding port throughput and stabilizing container freight conditions deliver the greatest resilience gains, while monitoring bulk/energy linkages is essential when macro shocks synchronize across markets. Full article

To quantitatively evaluate the hydrological regime dynamics in West Dongting Lake over the past seven decades, this study utilizes daily average water level series (1955–2024) from key control stations (Nanzui and Xiaohezui) to analyze variations in water level and discharge through change-point detection methods, adopting the water level difference between Xiaohezui and Nanzui as a pivotal indicator of hydrological changes; the IHA–RVA framework is then applied to comprehensively assess the degree of alteration in hydrological indicators before and after identifying change points, demonstrating the following: (1) declining trends in water level/discharge at both stations—primarily attributable to reduced inflows from the Songzi and Hudu Rivers—underwent abrupt shifts in 1983 and 2003, while the water level difference displayed an increasing trend with a change point in 1991; (2) the overall degree of hydrologic alteration (DHA) was moderate, with enhanced variability during T2 (2003–2024) relative to T1 (1983–2003), notably for discharge at Nanzui and water level at Xiaohezui; (3) reduced discharge in the Songzi and Hudu Rivers primarily drives the decreased outflow from West Dongting Lake. In the Li and Yuan basins during period T1, anthropogenic factors dominated runoff alterations. During T2, anthropogenic contributions accounted for 76.27% and 48.67% of runoff changes, respectively, resulting in reduced runoff volumes under equivalent precipitation inputs. (4) Under fixed water level differences, a significant positive correlation exists between discharges at Xiaohezui and Nanzui stations. Greater discharge flows downstream through the flow channel adjacent to NZ at West Dongting Lake’s outlet. Collectively, these findings establish a technical foundation for assessing the impact of hydrological regimes and aquatic ecological security in Dongting Lake, thereby advancing sustainable water resource utilization across the basin. Full article