Search Results (1,925)

Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5–50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts. Full article

This paper presents the development of a fuzzy-PID control able to adapt to several robot–patient interaction modes by monitoring patient evolution during the rehabilitation procedure. This control system is designed to provide targeted rehabilitation therapy through three interaction modes: passive; active–assistive; and resistive. By integrating a fuzzy inference system into the classical PID architecture, the FPID controller dynamically adjusts control gains in response to tracking error and patient effort. The simulation results indicate that, in passive mode, the FPID controller achieves a 32% lower RMSE, reduced overshoot, and a faster settling time compared to the conventional PID. In the active–assistive mode, the FPID demonstrates enhanced responsiveness and reduced error lag when tracking a sinusoidal reference, while in resistive mode, it more effectively compensates for imposed load disturbances. A rehabilitation scenario simulating repeated motion cycles on a healthy subject further confirms that the FPID controller consistently produces a lower overall RMSE and variability. Full article

This study aims to analyze the impact of the U.S. Federal Reserve System’s monetary policy on major cryptocurrencies. Specifically, it explores whether the effects differ between volatile cryptocurrencies, such as Bitcoin and Ethereum, and the stablecoin Tether. To this end, we utilize an autoregressive distributed lag (ARDL) bounds testing approach, analyzing monthly data from January 2019 to April 2025. The empirical results indicate that the responses of volatile and stable cryptocurrencies to the Fed’s monetary policy differ. In the long term, the prices of Bitcoin and Ethereum tend to react positively to the Fed’s monetary policy changes, whereas Tether’s prices experience a negative impact. We recommend novel policy implications in this study based on these empirical findings. Full article

Data scarcity hampers the implementation of circular economy (CE) in rural historical small towns (HSTs) where traditional agricultural practices persist outside formal monitoring systems. In this regard, this study proposes and tests an estimation framework to quantify agricultural waste flows and energy recovery potential. The methodology combines waste generation coefficients from peer-reviewed literature with administrative data to generate actionable CE assessments. Application to four Sicilian HSTs within the Local Action Group (LAG) “Terre dell’Etna e dell’Alcantara” exhibits substantial waste generation potential despite their small size. The agricultural enterprises generate an estimated 6930–7130 tons of annual agricultural waste under moderate production scenarios, comprising grape pomace (3250 tons), pruning residues (3030 tons), and mixed processing wastes (650–850 tons). The energy recovery potential ranges from 20–30 TJ through direct combustion to 4.9–8.1 TJ via anaerobic digestion. Sensitivity analysis indicates balanced contributions from all three key parameters (enterprise density, yields, and waste coefficients), each accounting for 31–35% of output variance. The framework provides resource-constrained municipalities with a cost-effective tool for preliminary CE assessment, enabling identification of priority interventions without expensive primary data collection. From a managerial perspective, local administrators can leverage this tool to transform routine administrative data into actionable CE strategies. Full article

Driven by climate change and stringent ecological conservation policies, arid and semi-arid pastoral areas face acute grassland degradation and forage–livestock imbalances. In Sunan County (Gansu Province, China), herders have increasingly turned to off-site grazing—leasing crop fields in adjacent oases during autumn and winter—to alleviate local grassland pressure and adapt their livelihoods. However, the interplay between the evolving land use system (L) and this emergent borrowed pasture system (B) remains under-explored. This study introduces a coupled analytical framework linking L and B. We employ multi-temporal remote sensing imagery (2018–2023) and official statistical data to derive land use dynamic degree (LUDD) metrics and 14 indicators for the borrowed pasture system. Through entropy weighting and a coupling coordination degree model (CCDM), we quantify subsystem performance, interaction intensity, and coordination over time. The results show that 2017 was a turning point in grassland–bare land dynamics: grassland trends shifted from positive to negative, whereas bare land trends turned from negative to positive; strong coupling but low early coordination (C > 0.95; D < 0.54) were present due to institutional lags, infrastructural gaps, and rising rental costs; resilient grassroots networks bolstered coordination during COVID-19 (D ≈ 0.78 in 2023); and institutional voids limited scalability, highlighting the need for integrated subsidy, insurance, and management frameworks. In addition, among those interviewed, 75% (15/20) observed significant grassland degradation before adopting off-site grazing, and 40% (8/20) perceived improvements afterward, indicating its potential role in ecological regulation under climate stress. By fusing remote sensing quantification with local stakeholder insights, this study advances social–ecological coupling theory and offers actionable guidance for optimizing cross-regional forage allocation and adaptive governance in arid pastoral zones. Full article

With the increasing complexity of mine ventilation networks, the difficulty of regulating ventilation systems has significantly increased. Lagging regulatory responses are prone to causing problems such as airflow turbulence and insufficient air supply in air-required areas, which seriously threaten the safety of underground operations. To address this challenge, this paper introduces the MOBWO algorithm into the field of ventilation system air volume optimization and proposes a mine air volume optimization and regulation method based on MOBWO. This paper constructs a multi-objective air volume optimization model with the total power of ventilators and the complexity of air pressure regulation as the optimization objectives. Using indicators such as GD and IGD, it compares the performance of the MOBWO algorithm with mainstream optimization algorithms such as NSGA-II and MOPSO and verifies the practicality of the optimization method with the case of the Jinhua Palace Mine. The results show that the MOBWO algorithm has significant advantages over other algorithms in terms of convergence and distribution performance. When applied to the Jinhua Palace Mine, the air volume optimization and regulation using MOBWO can reduce the power of ventilators by 10.3–21.1% compared with that before optimization while reducing the complexity of air volume regulation and the time loss during air volume regulation. This method not only reduces the energy consumption of ventilators but also shortens the regulation timeliness of the ventilation system, which is of great significance for reducing the probability of accidents and ensuring the safety of personnel’s lives and property. Full article

To achieve climate goals and promote clean energy, China has introduced preferential VAT policies to promote the development of renewable energy power generation industries, but their actual impact on corporate profitability remains underexplored. This study innovatively applies a DID approach, enhanced with PSM and dynamic modeling, to evaluate the causal effects of VAT incentives on firm ROE. Using panel data from 98 listed power generation companies between 2010 and 2024, this study distinguishes treatment effects across the wind, solar, and hydrogen sectors, revealing significant heterogeneity. Unlike prior studies, it further investigates time-lagged impacts and fiscal efficiency indicators to assess policy sustainability. Results show that VAT incentives significantly enhance ROE for wind and solar firms, while the hydrogen sector exhibits weaker responses. These findings not only confirm the effectiveness of targeted tax incentives but also offer new insights for refining fiscal policies to better support sector-specific transitions toward renewable energy. This study provides empirical evidence for the design of China’s fiscal energy policy to maximize the growth of the renewable energy sector. More broadly, this study provides lessons for global green transition policies, illustrating how well-designed fiscal incentives can support sustainable energy development worldwide. Full article

Debt financing is important for financing major investments in the biopharmaceutical industry. Debt financing allows companies to raise funds without giving up ownership or control through indenture and covenants of the company. In this study, I analyze the effects of debt financing decisions on profitability in the biopharmaceutical industry. I find that short-term debt, long-term debt, and total debt negatively impact the return on assets (ROA) as a firm’s profitability measure. A comparison is made between American and European biopharmaceutical firms, and the result shows the negative effects of short-term and long-term debt on profitability persist more for US biopharmaceutical firms than European firms. Short-term and long-term debt both impact profitability negatively with 10-year lagged R&D intensity and financial distress. Short-term debt’s negative impact is stronger post-COVID-19, indicating increased financial strain. Long-term debt consistently affects profitability negatively, with relatively stable effects during the pre- and post-COVID-19 pandemic. Full article

Gray snapper (Lutjanus griseus; Family: Lutjanidae) local habitat preferences have been assessed, but the biotic and abiotic factors influencing age and growth rates in Mosquito Lagoon, Florida, have not been quantified. To address this knowledge gap, the goal of this study was to estimate mean age and growth rate of gray snapper, and use generalized linear mixed models to investigate if prey and/or other environmental factors (e.g., abiotic/biotic conditions, time, location, or habitat restoration status) impact size at both the lagoon- and habitat-specific scales. Age data were extracted via otolith microstructural analyses, and incorporated with size into a lagoon-scale linear growth model. Based on microstructural analyses, mean age of gray snapper at the lagoon scale was 175 ± 66 days (range = 56–350 days). The results indicate the most common life stage of gray snapper in Mosquito Lagoon is juveniles, with living shoreline habitats having a greater proportion of relatively young juveniles (111 ± 36 days) and oyster reef habitats having a greater proportion of relatively older juveniles (198 ± 58 days). The estimated growth rate was 0.43 mm/day. Body mass and body length were correlated positively with habitat quality and lagged salinity levels. Hence future studies should strive to characterize benthic habitat characteristics, and investigate biotic and abiotic factors that potentially influence gray snapper growth. Collectively, this study increases our understanding of environmental drivers affecting juvenile gray snapper development and shows that the restoration of benthic habitats can produce conditions conducive to gray snapper growth. The age-, size-, and habitat-specific growth rates of juveniles from this study can be incorporated into stock assessments, and thereby be used to refine and develop more effective ecosystem-based management strategies for gray snapper fisheries. Full article

This research developed a complex physical and mathematical model of the flat rolling theory problem. This model takes into account the influence of many parameters affecting the roll’s gripping capacity and the overall stability of the entire rolling process. It is important to emphasize that the method of the argument of functions of a complex variable does not rely on simplifying assumptions commonly associated with: the linearized theory of plasticity; or the decoupled solution of stress and strain fields. Furthermore, it does not utilize the rigid-plastic material model. Within this method, solutions are developed based on the complete formulation of the system of equations in terms of stresses and strains, incorporating constitutive relations, thermal effects, and boundary conditions that define a well-posed problem in the theory of plasticity. The presented applied problem is closed in nature, yet it accounts for the effects of mechanical loading and satisfies the system of equation. For this purpose, such factors as roll geometry, physical and mechanical properties of the rolled metal (including its fluidity, hardness, plasticity, and structure heterogeneity), rolling speed, metal temperature, roll lubrication, and many other parameters that can influence the process have been taken into account. Based on the developed mathematical model, a new, previously undescribed force factor significantly affecting the capture of metal by rolls and the stability of the rolling process was identified and investigated in detail. This factor is associated with force stretching of metal in the lagging zone—the area behind the rolls, where the metal has already left the deformation zone, but continues to experience residual stress. It was shown that this stretching, depending on the process parameters, can both contribute to the rolling stability and, on the contrary, destabilize it, causing oscillations and non-uniformity of deformation. The qualitative indicators of transient regime stability have been determined for various values of the parameter α. Specifically, for α = 0.077, the ratio f/α ranges from 1.10 to 1.95; for α = 0.129, the ratio f/α ranges from 1.19 to 1.95; and for α = 0.168, the ratio f/α ranges from 1.28 to 1.95. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

Globally, ecosystems are affected by climate change, human activities, and natural disasters, which impact ecosystem quality and stability. Vegetation plays a crucial role in ecosystem material cycle and energy transformation, making it important to monitor its resilience under disturbance stress. The Critical Slowing Down (CSD) indicates that as ecosystems near collapse, the autocorrelation of lag temporal increases and resilience decreases. We used the lag Temporal Autocorrelation (TAC) of long-term remote sensing Leaf Area Index (LAI) to monitor vegetation resilience in the Three Gorges Reservoir Area (TGRA). The Disturbance Event Model (DEM) was used to validate the CSD. The results showed the following: (1) The eastern TGRA exhibited high and increasing vegetation resilience, while most areas showed a decline. (2) Among the various vegetation types, forests demonstrated higher resilience than other vegetation types. (3) Precipitation, temperature, and soil moisture significantly influenced vegetation resilience dynamics within the TGRA. (4) For model accuracy, the CSD’s results were consistent with the DEM, confirming its applicability in the TGRA. Overall, the CSD when applied to long-term remote sensing data, provided valuable quantitative indicators for vegetation resilience. Furthermore, more CSD-based indicators are needed to analyze vegetation resilience dynamics and better understand the biological processes determining vegetation degradation and restoration. Full article

Vegetation growth is influenced not only by current climatic conditions but also by growth-enhancing signals and preceding climate factors. Taking the dominant species, Juniperus seravschanica Kom, in Tajikistan as the research subject, this study combines tree-ring width data with early xylem growth season data (from the start of xylem growth to the first day of the NDVI peak month), simulated using the Vaganov–Shashkin (V-S) model, a process-based tree-ring growth model. This study aims to explore the effects of vegetation growth carryover (VGC) and lagged climatic effects (LCE) on tree rings and the early xylem growth season at two different scales by integrating tree-ring width data and xylem phenology simulations. A vector autoregression (VAR) model was employed to analyze the response intensity and duration of VGC and LCE. The results show that the VGC response intensity in the early xylem growth season is higher than that of tree-ring width. The LCE duration for both the early xylem growth season and tree-ring width ranges from 0 to 11 (years or seasons), with peak LCE response intensity observed at a lag of 2–3 (years or seasons). The persistence of the climate lag effect on vegetation growth has been underestimated, supporting the use of a lag of 0–3 (years or seasons) to study the long-term impacts of climate. The influence of VGC on vegetation growth is significantly stronger than that of LCEs; ultimately indicating that J. seravschanica adapts to harsh environments by modulating its growth strategy through VGC and LCE. Investigating the VGC and LCE of multi-scale xylem growth indicators enhances our understanding of forest ecosystem dynamics. Full article

This study aims to improve the accuracy and interpretability of deep groundwater level forecasting in Cangzhou, a typical overexploitation area in the North China Plain. To address the limitations of traditional models and existing machine learning approaches, we develop a Stacking ensemble learning framework that integrates meteorological, spatial, and anthropogenic variables, including lagged groundwater levels to reflect aquifer memory. The model combines six heterogeneous base learners with a meta-model to enhance prediction robustness. Performance evaluation shows that the ensemble model consistently outperforms individual models in accuracy, generalization, and spatial adaptability. Scenario-based simulations are further conducted to assess the effects of the South-to-North Water Diversion Project. Results indicate that the diversion project significantly mitigates groundwater depletion, with the most overexploited zones showing water level recovery of up to 17 m compared to the no-diversion scenario. Feature importance analysis confirms that lagged water levels and pumping volumes are dominant predictors, aligning with groundwater system dynamics. These findings demonstrate the effectiveness of ensemble learning in modeling complex groundwater behavior and provide a practical tool for water resource regulation. The proposed framework is adaptable to other groundwater-stressed regions and supports dynamic policy design for sustainable groundwater management. Full article

As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. Full article