Search Results (1,683)

Accurate forecasting of sediment accumulation under extreme hydrodynamic forcing is essential for coastal engineering design and harbor management. This study evaluates the performance of Dynamic Mode Decomposition (DMD), optimized DMD (optDMD), and optimized DMD with stability constraints (optDMDs) for reconstructing and forecasting sediment accumulation height fields at the Dilderesi River mouth under a 50-year return period flood scenario. Sediment height fields generated using Delft3D are represented through reduced-order modal decompositions and the truncation rank is determined based on reconstruction-error analysis. Although all formulations reproduce the training data with negligible error, their predictive behavior differs during temporal extrapolation. Standard DMD exhibits rapid error growth at longer lead times. The optDMD formulation improves short- and intermediate-horizon performance but shows gradual degradation at extended lead times. Optimized DMD with stability constraints provides the most consistent long-horizon forecasts, maintaining high Nash–Sutcliffe efficiency and low RMSE across the full 9 h prediction interval. Examination of the continuous-time eigenvalue distributions and modal dynamics indicates that spectral characteristics of the reduced-order representation govern forecast robustness. The results demonstrate that enforcing spectral stability within reduced-order frameworks substantially enhances morphodynamic forecasting reliability under extreme flood conditions. The proposed approach provides a computationally efficient and physically consistent tool for sediment dynamics prediction in coastal engineering applications. Full article

Publicly available electricity datasets from operational industrial facilities remain limited due to instrumentation cost, retrofit complexity, and data governance constraints. This paper presents an openly accessible dataset of asset-level electrical energy measurements collected from a medium-scale industrial manufacturing facility over an approximately one-year observation window, with staged commissioning resulting in heterogeneous temporal coverage. The dataset includes time-series measurements from production machinery, auxiliary systems, and distribution-level assets instrumented using a heterogeneous fleet of Ethernet and RS-485 energy meters integrated via industrial gateways and programmable logic controllers. Measurements were acquired via a SCADA-based logging infrastructure and exported from an operational SQL historian. The publicly released dataset comprises fixed 15 min aggregated energy and power metrics derived from high-frequency SCADA telemetry. In its released ALL-phase representation, the dataset comprises measurements from 43 monitored assets and 1,039,873 15 min windows, corresponding to 2.96 GWh of measured electrical energy. Mean window-level data coverage is 99.99%, and 97.72% of ALL-phase windows satisfy the dataset’s reliability criterion. Interval records include energy consumption, demand, data coverage metrics, and reliability indicators. The dataset reflects real-world industrial monitoring conditions, including mixed communication pathways and irregular sampling behaviour, and is intended to support research in industrial energy analytics, data quality assessment, load profiling, and operational energy modelling. Full article

Human motivation is governed by a long-memory cognitive process in which the depth of temporal integration—how far into the past the system draws upon accumulated experience—is not fixed, but dynamically compressed under cognitive stress. Despite extensive empirical evidence that acute stress impairs working memory and narrows temporal integration in decision-making, no existing mathematical framework has formally coupled the memory depth of the governing operator to a physiologically grounded stress indicator. To address this gap, we propose a stress-adaptive variable-order fractional model for motivational intensity $M\left(t\right)$, in which the Caputo fractional order $\alpha \left(t\right)$ varies inversely with an aggregated stress indicator $\sigma \left(t\right)$ through the Hill-type coupling $\alpha \left(t\right)={\alpha }_{min}+({\alpha }_{max}-{\alpha }_{min})C/(C+\sigma \left(t\right))$, thereby encoding the empirically documented shift from deep integrative to shallow heuristic processing as cognitive load increases. Rather than deriving the model by algebraic manipulation of a differential equation, we formulate it directly as a causally consistent type-III Volterra integral equation, in which the memory kernel is evaluated at the history time s, ensuring that the weight assigned to each past state reflects the memory depth that was physiologically active when that state was experienced. Well-posedness is established rigorously via the Banach fixed-point theorem with explicit contraction constants, uniform boundedness and non-negativity of solutions are derived through the fractional Gronwall inequality, and numerical solutions are computed using an Adams–Bashforth–Moulton predictor–corrector scheme adapted to the variable-order kernel. Five numerical experiments demonstrate that stress-induced variation in $\alpha \left(t\right)$ produces qualitatively richer dynamics compared with the tested constant-order baselines: the proposed model achieves a steeper peak decline rate (0.48 versus 0.19–0.45), a larger burnout gap (3.15 versus 1.92–2.81), and faster recovery to ninety percent of peak motivation (4.2 versus 3.9–7.3 time units), while the empirically observed numerical convergence approaches $O\left(h^2\right)$ for sufficiently small step sizes. The framework offers a principled phenomenological substrate for memory-adaptive cognitive modelling, with direct implications for stress-aware intelligent tutoring systems that are capable of inferring $\alpha \left(t\right)$ in real time from biometric signals such as heart rate variability or galvanic skin response, and adjusting instructional complexity accordingly. Empirical calibration against learning-analytics and psychophysiological datasets, together with stochastic extensions for probabilistic burnout-risk prediction, are identified as immediate priorities for future research. Full article

Rapid urbanization and complex topography complicate Urban Heat Island (UHI) spatio-temporal dynamics. Traditional models and coarse-resolution imagery often fail to capture fine-scale, spatially non-stationary seasonal driving mechanisms. This study investigates the multi-dimensional drivers of surface thermal dynamics in Kunming, a typical low-latitude plateau city, using seasonal median LST composite (2018–2025). Integrating eXtreme Gradient Boosting (XGBoost) with eXplainable Artificial Intelligence (XAI) models decoupled the nonlinear impacts of these drivers. Results reveal a seasonal thermal dichotomy: Summer exhibits the most intense UHI effect with extreme peak temperatures, while Spring presents an anomaly where natural and vegetated Local Climate Zones (LCZs) show pronounced warming. SHapley Additive exPlanations (SHAP) analysis identified a seasonal rotation: anthropogenic and structural factors dominate Summer and Autumn warming, whereas natural and topographic regulators govern Spring and Winter. GeoShapley deconstruction demonstrated strong spatial non-stationarity. Building-density warming is amplified in poorly ventilated urban cores, and fragmented vegetation’s cooling is offset by anthropogenic heat during peak summer. This study provides new insights into the seasonal drivers of urban thermal environments in plateau cities. Full article

As China’s first World Heritage Mixed Property site, Mount Tai enjoys international renown, with its historic buildings serving both as the central carriers of its cultural heritage and as significant tourism resources. Existing studies have predominantly emphasized the form, scale, and construction techniques of individual buildings or architectural complexes, while less attention has been given to the overall spatial pattern shaped by the interplay of natural and social environments and to the mechanisms underlying its formation. Taking the administrative area of Tai’an City as the study extent, this research selects 451 officially protected historic buildings, classified by period and type, and employs GIS-based spatial analysis and statistical methods to examine their spatiotemporal distribution patterns and influencing factors. The results indicate the following. (1) The temporal distribution exhibits an И-shaped fluctuation pattern, with ancient architecture and ancient sites together accounting for nearly 60% of the total and constituting the core resource categories. This distribution curve is shaped jointly by preservation conditions, social stability, and heritage designation preferences. (2) The spatial distribution displays a pronounced clustering pattern, with the kernel density core shifting over forty kilometers from southwest to northeast, generating an evolutionary trajectory from Dawen River basin agglomeration to Mount Tai mountain belt agglomeration. (3) The overall pattern is associated with both natural and anthropogenic factors. During the early stages, natural conditions such as hydrology and topography provided foundational constraints, whereas in later periods, human factors, including fengshan ritual culture, religious activities, economic development, and institutional governance, exhibit increasingly apparent associations with the distribution pattern. Based on these findings, this study proposes a strategic spatial framework comprising one cultural pilgrimage ring and four thematic corridors, which translates the spatial analytical results into planning implications for the regional integration of historic building resources, and discusses differentiated conservation strategies, thereby providing an analytical foundation and a reference pathway for the dissemination of Mount Tai culture and the sustainable development of heritage tourism. Full article

In the context of global digital transformation, scientifically examining the spatio-temporal evolution patterns and transition mechanisms of the digital economy at the municipal level is crucial for promoting coordinated regional development. This study takes 281 prefecture-level cities in China from 2011 to 2023 as its research units. Exploratory Spatio-Temporal Data Analysis (ESTDA) is employed to analyze its spatio-temporal dynamics, while a panel quantile regression model nested with spatio-temporal transition types is used to reveal the driving mechanisms. The findings indicate that (1) the overall development level of China’s municipal digital economy has steadily increased, yet significant regional heterogeneity persists, characterized by a pattern of “eastern leading, central fastest-growing, and western lagging,” with considerable room for overall improvement. (2) The digital economy exhibits a significant positive spatial correlation. High–high agglomeration areas remain stable in the southeastern coast, whereas low–low agglomeration areas are concentrated in the central-western and northeastern regions. The spatial pattern demonstrates strong stability and path dependence. (3) LISA time paths reveal drastic changes in local spatial structures in provinces such as Heilongjiang, Inner Mongolia, Hubei, Guangdong, and Guangxi, while East and Central China remain relatively stable. Tortuosity analysis indicates that spatial linkages in the western region are becoming active yet unstable. (4) The quantile regression nested with transition types identifies four mechanisms: “Economic development-Technological innovation” serves as the fundamental driving mechanism across all regions. Low-quantile areas face a complex situation with dual suppression from “opening-up and urbanization” coexisting with drivers from “human capital, government intervention, and industrial structure.” High-quantile areas are synergistically driven by “urbanization, human capital, government intervention, and advanced industrial structure.” This study provides a decision-making reference for overcoming the dilemma of “low-level club convergence” in digital economy development and formulating differentiated regional policies. Full article

Airborne microplastics (MPs) are increasingly recognized as a pervasive pollutant with potential implications for environmental and human health. Despite growing concern, the influence of seasonal dynamics and environmental conditions on MP distribution remains poorly understood. This study investigates the temporal variability and environmental drivers of MPs across outdoor settings, highlighting how factors such as temperature, wind speeds, and precipitation modulate their behaviors. Using a combination of shielded gravitational deposition sampling (Sigma-2) and bulk deposition sampling over four seasons, coupled with μ-FTIR single particle analysis, we quantified MP abundance, size distribution, morphology, and polymer composition across contrasting environments. Deposition fluxes differed between samplers, with bulk samplers yielding 131–1589 MP/m²/d and Sigma-2 samplers yielding 4208–39,126 MP/m²/d. Multivariate analyses indicate that temperature was significantly correlated with MP loading in the Sigma-2 sampler, whereas precipitation effects were not detectable within the temporal resolution of our dataset. Polymer profiles differed between samplers, with Sigma-2 samples enriched in polyamide (PA) and resin-type particles, and bulk samples containing higher proportions of rubber and acrylate. Spherical and irregular particles were the predominant morphologies across both samplers. Together, these findings provide new insights into the environmental controls governing airborne MP deposition and underscore the need for long-term, meteorology-integrated, and methodologically standardized monitoring strategies to improve exposure assessment and inform mitigation efforts. Full article

This paper presents a novel proportional–integral–derivative (PID) control framework for first $\alpha$-order systems evolving in fractal time. The main contribution is the extension of classical control theory to systems exhibiting anomalous temporal scaling by employing local fractal derivatives. In contrast to fractional-order PID (FOPID) approaches, which primarily model memory effects, the proposed fractal PID framework captures time-scaling behavior arising in non-smooth environments, such as viscoelastic friction and irregular contact surfaces. The closed-loop dynamics are formulated as a second $\alpha$-order fractal differential equation, from which a characteristic equation is derived to establish conditions for asymptotic stability. It is shown that, for a constant reference input and positive controller gains, the tracking error converges to zero as $t\to \infty$. In addition, a quantitative performance analysis demonstrates that the fractal-order $\alpha$ governs temporal stretching: smaller values of $\alpha$ lead to increased rise and settling times and reduced oscillation frequency. The effectiveness of the proposed approach is illustrated through applications to a thermal system with fractal heat input and robotic actuators operating in irregular environments. These results highlight the potential of fractal-time control as a systematic framework for modeling and controlling dynamical systems with non-integer temporal structure. Full article

Background: Adipogenesis is governed by a complex interplay between transcriptional regulation and epigenetic remodeling. While many transcriptional pathways have been well characterized, less is known about how chromatin-level regulation shapes the timing of gene expression, particularly in large animal models such as pigs. In this study, we investigated histone modification patterns associated with four key adipogenic transcription factor genes—PPARG, GATA2, CEBPA, and CEBPB—in porcine mesenchymal stem cells (MSCs) undergoing adipogenic differentiation. Methods: Using RT-qPCR and ChIP-qPCR, we profiled gene transcription levels and epigenetic marks, including promoter- and exon-specific enrichment of the activating histone marks H3K9ac and H4K8ac, as well as the repressive mark H4K20me3, across six time points (day 0, 2, 4, 6, 8, and 10). Results: Although PPARG and GATA2 are located in close proximity on porcine chromosome 13, they exhibited distinct histone modification profiles. PPARG showed progressive promoter acetylation (H4K8ac) accompanied by transcriptional activation, whereas GATA2 displayed decreased exon acetylation (H3K9ac) associated with declining expression. In contrast, the H4K20me3 profile was similar for both genes, suggesting no direct association with their transcriptional activity. Interestingly, CEBPA (chromosome 6) and CEBPB (chromosome 17) exhibited temporally distinct histone modification patterns consistent with their roles in intermediate and early stages of adipogenic differentiation, respectively. Increased enrichment of the H3K9ac mark preceded the rise in transcript levels of the analyzed genes. Promoter regions showed higher enrichment of H4K8ac compared with exonic regions. A higher level of H4K20me3 was also observed for CEBPA and CEBPB than for PPARG and GATA2, which appeared to be more related to chromosomal localization than to direct transcriptional regulation. Conclusions: Together, these results reveal complex interactions between transcriptional dynamics and selected histone modifications that depend on both the gene analyzed and the stage of adipocyte differentiation. This study provides new insights into the epigenetic regulation of porcine adipogenesis and highlights chromatin context as an additional layer influencing transcriptional control. Full article

The Fenwei Plain (FWP) is a typical basin-type region in northern China characterized by complex PM_2.5 and O₃ composite pollution. Based on hourly monitoring data from 11 cities (2015–2024), this study integrated the Mann–Kendall test, standard deviation ellipse (SDE), spatio-temporal cross-correlation function (STCCF), and spatio-temporal geographically weighted regression (GTWR) to systematically analyze decadal pollution patterns and coupling mechanisms. Results revealed a profound transition from particulate-dominated to photochemical-regime pollution: PM_2.5 concentrations decreased significantly by 32%, whereas MDA8 O₃ rose by 47%. Spring emerged as the critical compound pollution window, accounting for 66.7% of simultaneous exceedances. Spatially, both pollutants maintained a consistent Northeast–Southwest orientation, with PM_2.5 hotspots concentrated along the Weihe River Valley. Cluster analysis categorized the 11 cities into O₃-dominant, compound-high-risk, and PM_2.5-dominant clusters. Furthermore, a dominant positive synergy was observed on an annual scale, although a localized “see-saw” effect occurred at a 150–200 km distance with a 3-year lag. The GTWR model demonstrated high robustness (R²: 0.75–0.86), underscoring the influence of localized driving forces. These findings provide a scientific basis for synergistic governance and precision air quality management in northern basin-type regions. Full article

The implementation of the national emergency industry demonstration bases’ policies is a new way to achieve safety production governance and a key factor in improving the effectiveness of national safety production governance. This study regards China’s national emergency industry demonstration bases’ policies as a quasi-natural experiment. Based on panel data from 31 provinces in China from 2010 to 2022, a multi-period difference in differences (DID) model is conducted to systematically evaluate the impact and mechanism of this policy on China’s safety production governance. The results show that this policy significantly reduced the death rate of safety production accidents with a GDP of 100 million yuan and has a significant governance improvement effect. Further analysis of the mediating effect shows that policies mainly exert governance effects by increasing public safety financial investment and promoting innovation output. The heterogeneity analysis results indicate that policy effects are more significant in regions with weaker energy-resource industrial bases and lower levels of digital development, suggesting that the marginal governance benefits of policies are mainly concentrated in areas with relatively weak supporting conditions for safety governance. This study makes three primary contributions to the literature. Theoretically, it expands the safety governance paradigm by shifting the focus from traditional administrative “command and control” regulations to market-driven industrial agglomeration. Methodologically, by utilizing a multi-period DID model, it overcomes endogeneity issues prevalent in prior correlation-based studies to rigorously identify causal effects. Empirically, it opens the “black box” of policy transmission by validating dual pathways—fiscal resource allocation and technological innovation—while highlighting a critical “filling the gap” marginal utility effect in resource-constrained regions. This study empirically reveals the mechanism and context-dependent characteristics of industrial policies in safety governance, providing empirical evidence for understanding the inherent logic between industrial policies, public safety governance, and regional sustainable development. It offers practical insights for optimizing the precise implementation and resource allocation of emergency industrial policies to foster socially sustainable and resilient industrial growth. Full article

Hydrodynamic efficiency in wetland systems is governed by the complex interaction between fluid flow and vegetation density. This study quantifies the impact of seasonal emergent vegetation growth on solute transport in a V-shaped flume. Using high-resolution tracer data from high-density (January) and low-density (November) conditions, we characterized hydraulic parameters, longitudinal velocity (v), and dispersion (D), across an upstream conduit (Reach 1) and a downstream retention zone (Reach 2). Results revealed that in January, Reach 2 exhibited massive hydraulic retardation (v ≈ 1.8 cm s⁻¹) and extensive non-Fickian tailing (variance > 30,000 s²), maintaining an idealized retentive state (Pe ≈ 20). Conversely, seasonal biomass reduction in November resulted in lower variance (≈16,500 s²) and drastically increased the risk of extreme advective bypass (Pe > 500). These findings provide critical empirical validation for macro-scale models like the Dynamic Model for Stormwater Treatment Areas (DMSTAs). Specifically, the massive temporal variance observed during the retentive state yielded an empirical Tanks-in-Series value of N ≈ 5.7, directly validating standard DMSTA defaults for dense emergent marshes. Furthermore, the Transient Storage Model (TSM) storage ratio (As/A) offers a quantitative mechanism to penalize modeled void fractions, accounting for vegetative “dead zones.” By integrating these flume-derived metrics, wetland managers can optimize hydraulic designs and improve the prediction of treatment efficiency across seasonal variations. Full article

The Beijing–Tianjin–Hebei (BTH) coordinated-development strategy provides a county-level setting for examining how transport-led regional restructuring reshaped the relationship between human activity and land–environment conditions. Using a balanced panel of 200 county-level units from 2010 to 2020, we work with two linked subsystems: the human-activity subsystem (H), which combines transport integration and economic upgrading, and the land–environment subsystem (L), which combines land-use transition and ecological response. Pooled entropy weighting, a coupling-coordination index, spatial autocorrelation analysis, and fixed-effects differential-response models are used to trace temporal change, spatial clustering, and post-2014 heterogeneity within BTH. Mean coupling coordination (D) rose from 0.5430 to 0.6012, but the increase came mainly from the rise of H, while L changed only slightly. Positive spatial autocorrelation persisted throughout the period. Counties in the Beijing–Tianjin ring kept higher absolute coordination levels, yet after 2014, they improved more slowly than non-ring counties because land–environment adjustment lagged behind changes within H. Relative to key ecological function zones, agricultural counties—and to a lesser extent urbanized counties—posted faster gains in D, again mainly through H. The results show that in BTH, regional integration did not move the two subsystems in lockstep: transport reorganization and economic upgrading advanced faster than land–environment adjustment, so durable county coordination still depended on land governance, ecological regulation, and policies matched to territorial functions. Full article

Understanding carbon dioxide (CO₂) emissions from buildings is critical for shaping effective policies toward sustainable urban development. Previous studies mainly applied bottom-up methods for small areas or top-down downscaling at national, provincial or grid scales. However, limited research has explored the relationship between building functions and CO₂ emissions at a larger scale. To bridge this gap, this study employed ridge regression to disaggregate monthly CO₂ emissions to the level of different functional buildings across England in 2022 and investigated the relationship between building functions and CO₂ emissions. Results show that commercial buildings rank highest in CO₂ intensity, reaching 1.49 kg per volume in February, while residential buildings rank lowest, reaching 0.25 kg per volume in July at the national scale, and industrial buildings have the largest total emissions. In addition, regional disparities in economic development and industrial structure contribute to emission differences among buildings of the same function. Temporally, all functional buildings exhibited lower emissions during summer compared to winter. Overall, this study offers a scalable and interpretable framework for understanding urban carbon emissions at high spatial and functional granularity. The findings may offer valuable insights to support government decision-making in urban planning and spatial policy design, thereby contributing to low-carbon development goals. Full article

In Seoul, neighborhood-scale commercial districts, known as Golmok commercial districts, are small-scale retail areas focused on local daily life but also play a significant role in the city’s economy. Existing classification strategies for supporting Seoul’s Golmok commercial districts primarily rely on static, administrative data, failing to sufficiently capture actual citizen usage patterns. This deficiency limits the effectiveness of revitalization efforts. This study employs a two-timescale analysis of de facto population data to build a more dynamic typology of Seoul’s Golmok commercial districts. An unsupervised machine learning approach, specifically time-series K-means clustering, was applied to both weekly (short-term) and multi-year (long-term) time series data. This enabled us to classify 1090 districts into 16 distinct types. This more granular typology reveals significant heterogeneity masked by the Seoul Metropolitan Government’s current system, which groups these districts into only four broad categories. Our results show that while a minority of districts maintain stable activity, many exhibit patterns of long-term decline or significant fluctuation, underscoring the diverse and dynamic nature of these areas. The short-term analysis also captures temporal variations in population activity. The proposed typology may offer a foundation for near real-time monitoring and more proactive policy interventions to support urban economic sustainability. Full article