Search Results (3,748)

Against the backdrop of significant climate change, resource constraints, and industrial upgrading, optimizing the coupling and coordination of the Water–Energy–Food (WEF) system in Northeast China is crucial for ensuring regional security and sustainable development. Existing research lacks long-term continuous analysis and inter-provincial comparisons. This article utilizes data from 2005 to 2023 to evaluate the development of the three provinces of Northeast China using a framework of 24 indicators covering safety, coordination, and resilience. Methodologies employed include the entropy weight method, the coupling coordination model, and the constraint model. The results show that: (1) The overall development level fluctuates with an overall upward trend, reaching a medium-coordinated level, and there are notable differences between provinces. (2) The coordination levels among provinces initially diverged but later converged, evolving from near dysfunction to a state of moderate coordination. Additionally, a bidirectional reinforcement mechanism has formed between system security and coupling coordination. (3) The key obstacles are deep-rooted in the system’s structure and have cross-provincial implications due to interconnected infrastructure, among which energy self-sufficiency and water-use efficiency are the primary constraints. (4) Resilience serves as a key mediating variable in regulating the relationship between security and coordination within the WEF system. In order to achieve a high level of coordination between WEF systems, it is necessary to formulate tailor-made subsystem governance policies, enhance the technological empowerment of water and energy conservation and efficiency improvement, and promote the development of resilient infrastructure. This integrated approach could systematically resolve resource competition conflicts, thus enhancing the overall resilience and sustainability of regional development. Full article

As a key indicator of air quality, effective forecasting of ${\mathrm{PM}}_{2.5}$ concentration can provide key technical support for the scientific and precise implementation of air pollution prevention and control. However, predicting ${\mathrm{PM}}_{2.5}$ concentrations faces challenges such as multiple influencing factors, long-term temporal dependencies, and inherent nonlinearity. Furthermore, traditional Long Short-Term Memory (LSTM) networks not only fail to effectively grasp the dependency relationships in long-time-span data, but also encounter difficulties in fully integrating and exploiting the information of numerous influencing factors. In order to solve these problems, a novel prediction model (OVMD–PeepholeLSTM–attention) for ${\mathrm{PM}}_{2.5}$ concentration was presented in this study, which includes Peephole Long Short-Term Memory (PeepholeLSTM), optimal variational mode decomposition (OVMD) and an attention mechanism (AM). In this study, K modal components result from the initial decomposition of ${\mathrm{PM}}_{2.5}$ monitoring data using OVMD. The obtained components are then individually predicted by the PeepholeLSTM–attention model, and the final prediction is reconstructed. The proposed model was comprehensively evaluated on ${\mathrm{PM}}_{2.5}$ concentration monitoring data sets from Guangzhou and Shenzhen in China from 2020 to 2022, through a series of comparative experiments. The model proposed in this study is shown by experimental results to reduce mean absolute error (MAE) by approximately 39%, root mean square error (RMSE) by 45%, and increases the fitting coefficient ($R^2$) by 0.0457 in Guangzhou compared to the single PeepholeLSTM model. The corresponding improvements in Shenzhen are 45% for MAE, 51% for RMSE, and 0.0765 for $R^2$. This indicates that the model proposed in this paper exhibits higher accuracy in terms of predicting ${\mathrm{PM}}_{2.5}$ concentrations, and the research results can provide a basis for quantitative assessment and scientific decision-making for the sustainable development of urban ecological environments. Full article

This study analyses the long-run relationship between governance quality and renewable energy development using a global panel of 174 countries over the period 2000–2023. The objective is to assess whether institutional quality systematically influences renewable energy deployment across heterogeneous development contexts. The empirical analysis employs a panel autoregressive distributed lag (PMG-ARDL) framework, which accommodates mixed integration orders and allows for heterogeneous short-run dynamics while imposing homogeneity on long-run coefficients. Renewable energy consumption, measured as the share of renewable energy in total final energy consumption, is modelled as a function of governance quality indicators, economic development, and environmental pressure, with trade openness and foreign direct investment included as control variables. Panel unit root tests indicate a mixture of I(0) and I(1) variables, supporting the use of the ARDL framework, while panel cointegration tests provide strong evidence of a stable long-run relationship in the estimated model. The results reveal a statistically significant long-run association between governance quality and renewable energy development, although the magnitude and direction of the effects vary across governance dimensions and development levels. In contrast, short-run effects are generally weak, suggesting that governance primarily shapes renewable energy outcomes through gradual, structural channels. These findings highlight the importance of institutional quality for long-term energy transition processes and provide empirically grounded insights for the design of energy and governance policies. The analysis reveals significant heterogeneity across development contexts: governance improvements yield positive effects on renewable energy adoption in low-income countries (β = +3.77), where institutional deficits constitute binding constraints, whilst the effect becomes negative in high-income economies (β = −11.87), reflecting diminishing returns and infrastructure lock-in. These findings suggest that developing countries should prioritise governance reforms—particularly Regulatory Quality and Political Stability—to accelerate energy transitions, whereas advanced economies should shift policy attention toward grid modernisation and market design. International organisations should adopt differentiated climate finance strategies matching institutional support to the development stage. Full article

Motivated by the close relationship between the matrix square root and the matrix sign function, this paper develops a new high-order iterative framework for computing the principal square root of a matrix and its inverse. The proposed approach is derived from a rational fixed-point iteration associated with a scalar nonlinear equation and is extended consistently to the matrix setting. The method is shown to be globally convergent and to achieve fourth-order convergence. Numerical experiments demonstrate that the new scheme outperforms several classical and Padé-based methods. Full article

The degradation of alpine meadows on the Qinghai–Tibet Plateau has seriously affected the structure and productivity of grassland communities. In this experiment, a sample area was set up in Keqihetan of Zexiong Village, Youganning Town, Henan County, Mongolian Autonomous Prefecture. The degraded alpine meadow was divided into three plaque types, bare patches (BP), short-term recovered patches (SRP), and long-term recovered patches (LRP), and Native alpine meadows (NM) as controls, in order to reveal the effects of grassland degradation on community structure and aboveground/belowground biomass allocation in alpine meadow. Here, we measured total biomass (TCB), aboveground biomass (AGB), belowground biomass (BGB), and root/shoot ratio (R/S) of alpine meadows on the Qinghai–Tibetan Plateau and investigated plant community cover and height. The results showed that with the restoration of the patchy alpine meadow, the height decreased first and then increased, the amount of AGB increased first and then decreased, while the coverage and BGB increased in turn, and BGB decreased with the deepening of soil depth. We also found that R/S decreased first and then increased with the patch recovery of the alpine meadow. The overall distribution of AGB and BGB belongs to allometric growth distribution, but the native meadow belongs to isometric growth distribution, while other recovery stages belong to allometric growth distribution. By studying the biomass and its distribution of degraded grassland, we can understand the impact of grassland degradation on the community structure and productivity of the alpine meadow. Full article

This research investigates the uptake of potentially toxic elements (PTEs) by Brassica rapa L. grown in volcanic and clay soils with high natural background levels of these elements, and assesses related human health risks. The study was conducted in two Italian regions that produce B. rapa L. for food use (Campania and Sicily). The results of this exploratory research indicate that the naturally elevated concentrations of PTEs in soils lead to correspondingly high levels of these elements in B. rapa L. The investigated soils exhibited marked chemical differences. Volcanic soils had higher Total Organic Carbon (TOC) and PTEs concentrations alongside lower pH and Cation Exchange Capacity (CEC) than clayey soils. In the investigated plants, PTEs accumulated mainly in roots and stems, with notable Hg levels in leaves. While As exceeded safety limits in only one edible sample from volcanic soil, Cd, Hg, and Pb frequently surpassed them. Health risk assessments revealed significant carcinogenic and non-carcinogenic risks from plants grown on volcanic soils, with levels that remain unacceptable even at low consumption rates. In contrast, lower risk levels are associated with the consumption of Brassica rapa grown in clay soils, with values that are generally considered tolerable at low consumption rates. The preliminary findings of this study highlight that natural soil enrichment can cause PTE levels in B. rapa L. that often exceed safe consumption thresholds. These results provide a foundation for future research aimed at more thoroughly investigating the mechanisms of metal uptake by edible plants in areas naturally enriched with PTEs in order to enhance the safety and sustainability of our food. Full article

Snow depth is a critical parameter for characterizing snow dynamics and water resources, and its accurate inversion is essential for hydrological processes, climate studies, and disaster prevention in cold regions. Based on long-term daily ground meteorological observation data from the hydrological years 1961 to 2015 at two meteorological stations in Mohe and Mishan, Heilongjiang Province, China, this study integrates physical parameters of snow density and snow albedo from the ERA5-Land reanalysis data to systematically compare the performance of three machine learning and three deep learning models in retrieving daily snow depth. Four feature combination schemes were designed to evaluate the contributions of meteorological factors, lagged snow depth terms, and snow physical parameters. The results indicate that, for both machine learning and deep learning models, the first-order lagged value of snow depth is the most important variable determining prediction accuracy. In terms of model performance, machine learning methods excelled, with XGBoost performing particularly outstandingly, achieving optimal prediction accuracy and stability under the best feature combination (coefficient of determination, R², reaching 0.989; root mean square error, RMSE, of 1.19 cm). Among deep learning methods, 1D CNN demonstrated strong local feature extraction capabilities, achieving prediction accuracy comparable to the best-performing machine learning model (R² of 0.9878, RMSE of 1.26 cm). Notably, models specifically designed for time-series data, such as LSTM (R² of 0.9848, RMSE of 1.41 cm), and the more complex 1D CNN-LSTM hybrid model (R² of 0.9803, RMSE of 1.60 cm) did not show significant advantages in this study. This indicates that model complexity and predictive performance are not simply positively correlated. Through comprehensive analysis of data from both stations, this study demonstrates that a prediction framework centered on historical snow depth as the core driving factor, combined with key meteorological elements, is highly robust. Although the inclusion of ERA5-Land snow physical parameters did not significantly improve model accuracy, it provides important insights for the future development of hybrid models that integrate physical mechanisms with data-driven approaches. The findings offer an effective solution for reconstructing long-term snow depth time series and hold significant application value for simulating cryospheric hydrological processes and studying climate change. Full article

Social factors profoundly shape the bereavement process for individuals who have lost someone to a drug-related death. In this study, we integrate qualitative (n = 19), quantitative (n = 5), and mixed-methods (n = 2) results from a large research project on drug-related bereavement and utilise Bronfenbrenner and Morris’s bioecological model as an analytical framework. The results of the project demonstrate that bereavement following a drug-related death is deeply rooted in social context, and they highlight that the process of grieving a drug-related death requires the navigation of complex personal, familial, and societal challenges. Sociocultural understandings of addiction and societal stigma must be addressed to create a more supportive environment for bereaved individuals. A more cohesive and responsive support system can be developed by understanding and acting at all levels of Bronfenbrenner and Morris’s model, encompassing individual competencies, organisational structures, broader social environments, and systemic policies. Focusing on a family and compassionate community approach, our research promotes an inclusive and empathetic societal response to these multifaceted losses. Furthermore, the importance of enhanced professional competencies, interdisciplinary collaboration, and the implementation of organisational change is emphasised in order to meet the needs of those affected by a drug-related death. Ultimately, social work can play a pivotal role in this context. Full article

This study systematically investigates the effects of rope length, lifting position, and load on the dynamic behavior and wind-induced fatigue life of flat-jib tower cranes. Firstly, natural frequencies of a representative crane (40 m tower, 66 m jib) were accurately identified via on-site stress measurements. Subsequently, a finite element model was developed to analyze free vibration characteristics under various operational conditions. Fluctuating along-wind loads were simulated using harmonic synthesis, and transient dynamic analysis provided the wind-induced response. Finally, fatigue life of critical components was assessed through rain-flow counting, S–N curves per Chinese and European standards, and Miner’s linear damage rule. The results indicate that the first-order natural frequency in the along-wind direction decreases by approximately 12.5% and 14.2% with increasing lifted load and rope length, respectively, while the frequency at the jib tip is reduced by up to 37% compared to that at the jib root. Structural responses are more pronounced in the along-wind direction, predominantly exciting the first-order mode. Under operational conditions, stress in the jib’s main chord increases by approximately 30% to 50%, whereas stress fluctuations in other jib sections remain minimal. The fatigue life of tower crane components decreases by 13% to 21% relative to the unloaded state, with rope length exerting a greater influence than lifting load magnitude. Full article

Plant-parasitic nematodes (PPNs) cause big crop losses globally. Safe/reliable methods for their durable management strategies can harness various beneficial relationships among the plant immune system and related microbiomes. Molecular mechanisms basic to these relations reveal wide arrays of significant roles for plant-healthy growth. This review focuses on such relations of microbiomes to prime and immunize plants against PPNs. It also highlights molecular issues facing PPN-resistant varieties with possible solutions such as genetic breeding/engineering, grafting, PPN-antagonistic root exudates, and novel resistant cultivars. These issues call for optimal uses of various widespread groups of microbiomes. Related plant signaling hormones and transcription factors that regulate gene expression and modulate nematode-responsive genes to ease positive/negative adaptation are presented. Exploring PPN-resistance genes, their activation mechanisms, and signaling networks offers a holistic grasp of plant defense related to biotic/abiotic factors. Such factors relevant to systemic acquired resistance (SAR) via plant–microbe interactions to manage PPNs are stressed. The microbiomes can be added as inoculants and/or steering the indigenous rhizosphere ones. Consequently, SAR is mediated by the accumulation of salicylic acid and the subsequent expression of pathogenesis-related genes. To activate SAR, adequate priming and induction of plant defense against PPNs would rely on closely linked factors. They mainly include the engaged microbiome species/strains, plant genotypes, existing fauna/flora, compatibility with other involved biologicals, and methods/rates of the inoculants. To operationalize improved plant resistance and the microbiome’s usage, novel actionable insights for research and field applications are necessary. Synthesis of adequate screening techniques in plant breeding would better use multiple parameters (molecular and classical ones)-based ratings for PPN-host suitability designation. Sound statistical analyses and interpretation approaches can better identify genotypes with high-level, stable resistance to PPNs than the commonly used ones. Linking molecular mechanisms to consistent field relevance can be progressed via dissemination of many advanced techniques. The CRISPR/Cas9 system has been effective in knocking out both the OsHPP04 gene in rice to confer resistance against Meloidogyne graminicola and the GhiMLO3 gene in cotton to minimize the Rotylenchulus reniformis reproduction. Its genetic modifications in crops synthesized “transgene-free” PPN-resistant plants without decreased growth/yield. Characterizing microbiome species/strains needed to prime and immunize plants requires better molecular tools for fine-scale taxonomic resolution than the common ones used. The former can distinguish closely related ones that exhibit divergent phenotypes for key attributes like stability and production of enzymes and secondary metabolites. As PPN-control strategies via tritrophic interactions are more sensitive to the relevant settings than chemical nematicides, it is suggested herein to test these settings on a case-by-case basis to avoid erratic/contradictory results. Moreover, expanding the use of automated systems to expedite detection/count processes of PPN and related microbes with objectivity/accuracy is discussed. When PPNs and their related microbial distribution patterns were modeled, more aspects of their field distributions were discovered in order to optimize their integrated management. Hence, the feasibility of site-specific microbiome application in PPN–hotspot infections can be evaluated. The main technical challenges and controversies in the field are also addressed herein. Their conceptual revision based on harnessing novel techniques/tools is direly needed for future clear trends. This review also engages raising growers’ awareness to leverage such strategies for enhancing plant resistance and advancing the microbiome role. Microbiomes enjoy wide spectrum efficacy, low fitness cost, and inheritance to next generations in durable agriculture. Full article

Threaded component fatigue failure is a severe issue in cyclically loaded mechanical systems, and the service life in these systems is controlled primarily by stress concentration at the thread root, especially in loading regimes dominated by bending. Rounded thread profiles such as knuckle threads have been thought to improve fatigue performance, although this is mostly due to the assumption being made on the basis of axial loading, the numerical stress analysis, and/or isolated stress-concentration analyses. This paper presents an experimental study on the fatigue behavior of knuckle-thread and conventional screw-thread specimens manufactured from AISI 1045 steel under rotating bending loading to determine the effects of thread geometry on fatigue life and damage mechanisms. Fatigue testing was conducted at varying stress levels to develop comparative stress–life (S–N) curves, the analytical relation being used in determining the stress-concentration factor, and standard literature techniques have been used in the analysis of fracture-surface in order to investigate the behavior of crack initiation and propagation. Results indicate that knuckle threads exhibit a lower stress concentration factor (K_t ≈ 1.59) than screw threads (K_t ≈ 2.11), resulting in longer fatigue life at the same nominal stress level, particularly in the high-cycle life regime. Fractographic research also indicates that knuckle threads enhance delayed crack initiation and more evenly distributed circumferential crack propagation, but screw threads show highly localized crack initiation and rapid radial propagation of cracks, resulting in earlier unstable fracture. These findings provide new experimental evidence that the improved fatigue performance of knuckle threads during rotating bending is linked to fundamental change in fatigue damage mechanism rather than to stress alleviation alone, thereby offering quantitative supporting guidance in designing fatigue-sensitive threaded components to experience cyclic bending. Full article

In adaptive optics systems, high spatial resolution detection is a core prerequisite for achieving accurate wavefront correction. High spatial resolution wavefront measurement based on the traditional Shack-Hartmann technique is limited by the density of the microlens array. In contrast, off-axis digital holography technology is applied in wavefront measurement systems of adaptive optics systems due to its advantages of high spatial resolution, non-contact measurement, and full-field measurement. However, during the demodulation of its interference fringes, the accurate extraction of the complex amplitude of the +1st-order diffraction order directly determines the precision of wavefront reconstruction. Traditional frequency-domain filtering methods suffer from drawbacks such as reliance on manual threshold setting, poor adaptability to irregular spectra, and localization deviations caused by multi-region interference, making it difficult to meet the dynamic application requirements of adaptive optics. To address these issues, this study proposes a spectrum extraction method based on the Canny operator for adaptive edge extraction and centroid localization. The method first locks the rough range of the +1st-order spectrum through multi-stage peak screening, then achieves complete segmentation of spectrum spots by combining adaptive histogram equalization with edge closing and filling, resolves centroid indexing errors via maximum connected component screening, and ultimately accomplishes accurate extraction through Gaussian window filtering. Simulation experimental results show that, in comparison with two classical spectrum filtering methods, the centroid estimation error of the proposed method remains below 0.245 pixels under different noise intensity conditions. Moreover, the root mean square error of the residual wavefront corresponding to the reconstructed wavefront of the proposed method is reduced by 89.0% and 87.2% compared with those of the two classical methods, respectively. We further carried out measurement experiments based on a self-developed atmospheric turbulence test bench. The experimental results demonstrate that the proposed method exhibits higher-precision spectral centroid localization capability, which provides a reliable technical support for the high-precision measurement of dynamic distortion induced by atmospheric turbulence. Full article

Rock pore–fracture systems exhibit inherent fractal characteristics, which exert a significant influence on fluid transport. In this study, coal rock is selected as the representative medium. Based on fractional calculus in physical fractal space, and by integrating operator algebra with the force–electric analogy method, a fractional order control equation is derived. To validate the proposed model, porosity measurements of coal and limestone were performed using the two-compartment Boyle’s law method and compared with conventional porosity calculation approaches. The results demonstrate that the fractional order model achieves a coefficient of determination (R²) of up to 0.99 for porosity and 0.98 for pressure, representing an improvement of approximately 0.07 over the exponential model. Moreover, the root mean square error (RMSE) of porosity is as low as 0.0008, while the RMSE of pressure is 0.0715, both significantly lower than those obtained using the exponential model. These results indicate that the fractional order model more effectively captures the non-Darcy flow behavior and the temporal evolution of porosity, providing substantially improved fitting accuracy. Further analysis reveals that the porosity–time relationship is jointly governed by fluid compressibility and pore compressibility under effective stress conditions. Comparative results across different lithologies reveal that the pore compressibility coefficient increases with porosity; for the same rock type, a higher coefficient implies a more complex pore structure and a longer equilibration time. Overall, the proposed fractional order framework provides a more accurate description of the fractal pore structures in rocks, establishing a clear link between microscale fractal geometry and macroscale fractional order response. Full article

Intergenerational wealth transfer represents a central mechanism through which genealogical bonds, family continuity, and economic stability are maintained across generations. This article examines inheritance law and financial regulation from a genealogical perspective, focusing on the role of family property as both a legal institution and a socio-economic structure rooted in kinship and lineage. By integrating approaches from genealogy, legal studies, and financial analysis, the study explores how inheritance frameworks shape intergenerational relations, preserve family identity, and influence patterns of economic inequality. The article analyzes inheritance law as a key instrument through which genealogical continuity is institutionalized, highlighting the ways in which legal norms regulate the transmission of assets, rights, and obligations within families. Particular attention is given to the interaction between financial regulation and family-based wealth, demonstrating how legal structures affect long-term economic sustainability and social cohesion. The study adopts a qualitative and theoretical methodology, supported by comparative references to selected legal traditions, in order to illustrate how inheritance systems reflect broader cultural, historical, and genealogical values. By situating inheritance and wealth transfer within the broader framework of genealogical relations, this article contributes to interdisciplinary discussions on family, law, and the economy. It argues that inheritance law should be understood not merely as a financial or legal mechanism, but as a genealogical process that shapes intergenerational bonds, social structures, and economic outcomes over time. Full article

This study examines the long-run relationship between trade openness, economic development, urbanization, and agricultural land use in developing economies. Using a panel of 20 developing countries covering the period 1990–2023, the analysis adopts a land systems perspective to assess how global economic integration influences land use dynamics. Agricultural land, measured as a share of total land area, is employed to capture changes in land allocation associated with structural transformation. Given the presence of cross-sectional dependence and slope heterogeneity, second-generation panel econometric methods are applied. Panel unit root tests indicate that all variables are integrated of order one, while the Westerlund cointegration test provides strong evidence of a long-run equilibrium relationship among the variables. Long-run coefficients are estimated using the Augmented Mean Group (AMG) estimator, which accounts for heterogeneous country-specific effects and unobserved common factors. Dumitrescu–Hurlin panel causality tests are further employed to explore causal interactions. The findings identify long-run structural interdependence and feedback patterns at the macro level rather than precise causal mechanisms or policy transmission channels. The results reveal a stable long-run linkage between agricultural land use, trade openness, income levels, and urbanization, with notable heterogeneity across countries. Bidirectional causality between trade openness and agricultural land use highlights feedback mechanisms between economic integration and land systems, underscoring the need to integrate land use considerations into trade and development policies. Full article