Search Results (495)

The critical role of green consumption in mitigating carbon emissions is widely acknowledged. As a prerequisite for green consumption, consumer green awareness (CGA) plays a pivotal role in advancing sustainable development. This study constructs a comprehensive indicator system for CGA from the three dimensions of “antecedent-behavior-outcome” and measures the CGA levels of 30 provinces in China from 2014 to 2022. Using the Theil index, kernel density estimation, Moran’s I, and Markov chain methods, we analyze its spatiotemporal evolution characteristics. Furthermore, spatial econometric models are applied to explore its driving factors. The results show that China’s CGA exhibits sustained growth during the study period, but regional disparities are widening, driven by inter-regional rather than intra-regional differences. Moreover, China’s CGA gradually demonstrates the long-tailed and multimodal distribution, accompanied by emerging spatial clustering effects. In terms of transition dynamics, CGA demonstrates a short-term “gradient lock”, which is substantially alleviated when spatial spillover effects are incorporated. Additionally, we find that economic development, the advancement of emerging industries, accelerated urbanization, emphasis on education, and policy guidance significantly promote CGA, while overconsumption inhibits CGA. Among these factors, economic development, informatization, e-commerce, education, and policy guidance show significant spillover effects. Full article

Plant-based diets are generally associated with a reduced risk of chronic diseases; however, the relationship between a vegan diet and genome integrity remains insufficiently characterized. In this cross-sectional study, we assessed primary DNA damage in peripheral blood cells of vegans and omnivores. A total of 62 apparently healthy adults were included: 31 vegans (median vegan diet duration 4.5 years) and 31 omnivores matched for sex and smoking status. DNA damage was assessed using the alkaline comet assay under standardized conditions and expressed as tail intensity (% tail DNA), tail length, tail moment, and total comet area. Tail intensity was significantly higher in vegans than in omnivores (B = 1.98; 95% CI 0.19 to 3.76; p = 0.031) after adjustment for age, physical activity, body mass index (BMI), and alcohol consumption. Within the vegan group, longer duration of adherence to a vegan diet was positively associated with tail intensity, independent of age (B = 0.23; 95% CI 0.03 to 0.43; p = 0.026). These findings suggest that adherence to a vegan diet and its duration may be associated with higher levels of primary DNA damage; however, these results should be interpreted with caution given the observational design and modest sample size. Full article

Selective inhibition of the tumour-associated carbonic anhydrase (CA) isoforms IX and XII, which are overexpressed in hypoxic tumours, has emerged as a promising strategy for the development of novel anticancer agents. Among the diverse CA inhibitors reported to date, coumarins have attracted particular attention. These chromenone derivatives, widely distributed in phytochemicals, display a broad range of biological activities and are known to act as suicide inhibitors of CAs. Following the tail approach, we designed a series of hybrid compounds combining a coumarin core with an N-arylthioureido scaffold located at the C-7 position and investigated how structural variations—including substituents on the coumarin and aromatic moieties, tether length, and urea/thiourea isosterism—influence their biological properties (CA inhibition and antiproliferative activity). Substituted coumarins at C-3 and C-4 were efficiently prepared via Pechmann condensation, while the thioureido motif was introduced using various aryl isothiocyanates as key synthetic intermediates. The lead compound, featuring a dimethylated coumarin, a pentyl linker, and an N-(p-tolyl)thioureido residue, inhibited the target enzymes in the low- to mid-nanomolar range (K_i = 6.0 and 49.9 nM, respectively), displaying selectivity indexes (S.I.s) surpassing those of the reference drug acetazolamide (AAZ). Moreover, it exhibited potent antiproliferative activity, with GI₅₀ values in the low micromolar range (1.9–3.5 µM) against both drug-sensitive and multidrug-resistant cancer cell lines. Label-free three-dimensional holotomographic microscopy revealed that this compound triggers slow apoptosis, leading to cell death after approximately 20 h of exposure. Full article

X-ray free-electron lasers (XFELs) have revolutionized structural biology by enabling “diffraction-before-destruction” and capturing the ultrafast dynamics of life. However, the intrinsic sparsity and noise of XFEL diffraction snapshots, often complicated by multi-lattice overlaps, create a formidable computational bottleneck that limits data utilization and structural fidelity. Here, we present MCDPS-SFX, a robust indexing framework based on a reference-based, whole-pattern matching principle integrated with parallelized iterative refinement. By exhaustively sampling orientation space and progressively rejecting outliers, MCDPS-SFX significantly outperforms legacy algorithms—more than doubling crystal yields in heterogeneous datasets (e.g., 21,807 vs. 8792 for MOSFLM)—and achieves highly competitive yields comparable to state-of-the-art indexers, such as extracting over 90,000 lattices in the lysozyme benchmark. We demonstrate its efficacy on standard benchmarks and technically demanding G-protein-coupled receptor (GPCR) systems, including the rhodopsin–arrestin complex and the glucagon receptor. MCDPS-SFX consistently produces high-quality data statistics, enabling the high-resolution visualization of functionally critical, flexible regions such as phosphorylated receptor tails. Our results provide a powerful tool for enhancing the scientific output of XFEL experiments, offering a robust alternative for maximizing information recovery from weakly diffracting or overlapping crystalline samples. Full article

Short-horizon equity forecasting remains challenging because daily prices are noisy, heavy-tailed, and subject to structural breaks and regime shifts. We develop a fully automated, reproducible, and leakage-controlled multi-model pipeline for daily forecasting with horizon-specific configuration selection. The task is formulated as predicting cumulative H-day log-returns from OHLCV-derived information and converting them to implied price forecasts. All model families share a homologated design: causal feature construction, a strictly chronological split with an explicit purging rule to prevent label-window overlap for multi-day targets, training-only robustification (winsorization and adaptive clipping), and a unified metric suite computed consistently in return and price spaces. The framework benchmarks transparent baselines (zero- and mean-return), gradient-boosted trees (XGBoost), and deep temporal models (LSTM and CNN/TCN). Lookback length $L\in \{60,180,500\}$ is selected via an internal walk-forward procedure on the pre-evaluation block, and final performance is reported on an external hold-out segment (last 15% of instances). Experiments on daily data for MT, DELL, and the S&P 500 index (through 3 February 2026) show that all families achieve similarly strong price-level fit at $H=1$, largely driven by persistence in the price process, while separation across families becomes more visible at $H=5$. However, predictive performance in return space remains weak, with $R^2$ close to zero or negative, and Diebold–Mariano tests do not provide consistent evidence of statistical superiority over naive benchmarks. Under an operational rule that minimizes hold-out RMSE on the price scale, selected models are asset- and horizon-dependent, supporting horizon-wise selection rather than a single global architecture. Overall, the primary contribution lies in the proposed leakage-controlled evaluation and benchmarking framework rather than in demonstrating consistent predictive gains in financial time series forecasting. Full article

Copper flotation tailings are produced in large quantities during ore beneficiation and smelting, yet remain underutilized and can act as persistent sources of potentially toxic elements. Here, we combined XRD-based mineralogical characterization, ICP-OES quantification, Tessier sequential extraction, and pH-dependent batch leaching to elucidate metal occurrence, mobility, and associated ecological risk in tailings from Tongling, Anhui Province. This study systematically analyzed the mineral composition, potentially toxic elements content, chemical fractions, leaching behavior, and ecological risks of copper flotation tailings from the Shuimuchong tailings reservoir in Tongling, Anhui Province. XRD and XRF analyses revealed that calcite, quartz, and garnet were dominant mineral phases in the tailings. Elevated levels of Cu, Cd, Pb, Zn, and As were detected, some of which surpassed both local background concentrations and national soil quality standards. Most potentially toxic elements primarily existed in the residual fraction, indicating low mobility. Leaching experiments revealed that Zn, Cu, and As showed enhanced release under acidic conditions, making them priority risk elements during tailings acidification. Pollution index and ecological risk assessments indicated that the tailings were heavily contaminated, with Cu and Cd as the main risk contributors. The Risk Assessment Code (RAC) evaluation showed that Cd had the highest bioavailability and ecological risk. By clarifying the behavior of pollutants, this study contributes to the effective regulation of environmental hazards and the sustainable use of tailing materials. Full article

Forest structural complexity underpins habitat quality, microclimate buffering, and resilience, yet it remains poorly characterized across the Hindu Kush Himalaya (HKH) where field inventories and airborne LiDAR are difficult to scale across rugged terrain. Conservation planning and protected-area evaluation in the HKH therefore often rely on canopy height or cover proxies that do not directly represent vertical structural organization. Here we develop a repeatable, uncertainty-aware indicator of forest structural complexity from GEDI waveform LiDAR using the Waveform Structural Complexity Index (WSCI) and its prediction intervals. We first define a conservative analysis footprint (“trustable pixels”) by combining a woody-vegetation screen with minimum GEDI sampling support and canopy-stature plausibility, and by excluding the highest-uncertainty tail using a relative prediction-interval criterion. To separate complexity from canopy height, we model the HKH-wide expected WSCI–RH98 relationship and map height-normalized excess complexity (observed minus expected), identifying structural complexity hotspots and coldspots as the upper and lower tails of the excess distribution. Anomaly patterns are strongly organized along elevation and treeline-relevant belts and show coherent departures among ecoregions that persist after stratified adjustment for elevation and mean annual precipitation, indicating additional controls beyond broad environmental gradients. Protected areas exhibit systematically lower hotspot prevalence than surrounding landscapes, and within-elevation comparisons suggest this association is not explained by elevation alone, highlighting the need to interpret protected-area signals in the context of placement and land-use pressure. Overall, the anomaly atlas provides an operational indicator framework to stratify monitoring, prioritize field validation, and support the landscape-scale assessment of structural conditions beyond canopy height across one of the world’s most critical mountain forest systems. Full article

The life insurance sector plays a critical role in financial system stability but is inherently exposed to extreme market fluctuations due to long-term liabilities and asset–liability mismatches. This study investigates extreme losses in the growth rates of the JSE Life Insurance Price Index (LIPI) using the Generalised Extreme Value Distribution (GEVD) and the Generalised Pareto Distribution (GPD) under the Extreme Value Theory (EVT) framework. Monthly data from January 2000 to October 2023 were transformed into a loss series, and extreme events were captured using quarterly block maxima and a POT threshold at the 95th percentile. Model parameters were estimated through Maximum Likelihood Estimation, and downside risk was assessed using return levels, Value-at-Risk (VaR), and Tail Value-at-Risk (tVaR). The GEVD model produced a negative shape parameter, consistent with a bounded Weibull-type tail, while the GPD indicated a heavy-tailed distribution. Return level estimates show escalating loss magnitudes and widening uncertainty over longer horizons, reflecting the challenges of projecting rare events. Kupiec backtesting confirms the adequacy and reliability of the GEVD-based VaR across all confidence levels, whereas the GPD underestimates risk at lower thresholds. These findings indicate significant tail risk within the South African life insurance equity segment and underscore the importance of EVT-based risk measures for capital planning and regulatory oversight. The study contributes to financial risk modelling in the life insurance sector and offers practical insights for strengthening solvency assessment and enterprise risk management frameworks. Full article

A low-titanium-slag-based multi-solid-waste cementitious system was developed for cemented paste backfill. The cementitious binder was prepared from low-titanium slag (LTS), steel slag (SS), municipal solid waste incineration (MSWI) fly ash, and flue gas desulfurization gypsum (FGDG), while lead–zinc tailings were used as the aggregate for backfill materials preparation. The activation of low-titanium slag, proportion optimization, and strength development mechanisms were systematically investigated. Mechanical grinding effectively activated low-titanium slag, and its activity index reached 108% after 90 min of grinding at 28 d. Steel slag alone could not fully activate low-titanium slag in the ternary system, whereas the incorporation of MSWI fly ash significantly enhanced the synergistic activation effect. The quaternary system with 40% MSWI fly ash replacement showed higher cumulative heat release and better later-age strength. The optimum backfill proportion was a solid mass concentration of 81% with a binder-to-tailings ratio of 1:4, yielding a 28 d compressive strength of 11.07 MPa with satisfactory flowability and setting behavior. Microstructural results indicated that the continuous formation of ettringite and gel phases promoted pore refinement and matrix densification. Moreover, the leaching concentrations of Pb, Zn, Cr, and soluble Cl were all below the relevant groundwater quality limits. These results demonstrate a feasible route for the high-value co-utilization of low-titanium slag and MSWI fly ash in cemented backfill materials. Full article

This paper introduces the Copula Asymmetry Index (CAI), a rolling, rank-based measure of asymmetric tail dependence between equity returns and implied-volatility proxies. CAI is defined as the difference between the empirical frequency of joint “equity-down & volatility-up” tail events and that of the mirror state (“equity-up & volatility-down”) within a rolling window. Building on this core asymmetry measure, we develop CAI++, an implementation framework that transforms CAI into an operational defensive allocation signal through smoothing, standardization, delayed execution, hysteresis, and cost-aware portfolio mapping. Using daily data from 2000 onward across a broad cross-section of 50 equity-volatility pairs, we evaluate the CAI++ strategy against buy-and-hold equity, a 60/40 benchmark, an inverse-volatility risk-parity portfolio, and a moving-average timing rule. Cross-sectional results indicate that CAI improves terminal outcomes relative to equity-only exposure for most pairs and shows particularly strong performance versus 60/40 in both final wealth and Sharpe. However, CAI does not dominate structurally diversified low-volatility allocations: risk parity retains a pronounced advantage in downside risk and risk-adjusted metrics. Overall, the findings support CAI as a tail-aware overlay for equity-centric and balanced portfolios rather than a substitute for institutional low-volatility baselines. Full article

Biosurfactants offer a green, sustainable approach to many environmental bioremediations, especially for oil contamination. In this study, the aim is to evaluate the effectiveness of biosurfactants in accelerating hydrocarbon removal from mature fine tailings under anaerobic conditions. The bacteria were isolated from mature fine tailings and tested for biosurfactant production using different biosurfactant screening methods (i.e., blood agar, cetyltrimethylammonium bromide (CTAB) blue agar, oil displacement, and drop collapse). The most efficient strain showed high similarity to Stutzerimonas stutzeri by 16S rRNA gene sequencing. Results showed that this strain produces rhamnolipids with a critical micelle concentration (CMC) of 600 mg/L and a minimum surface tension of 38.70 ± 0.08 mN/m. Moreover, when supplemented with whey, the strain showed a high emulsification index of 24 toward toluene (66%) and hexane (60%). The bioremediation of mature fine tailings (MFTs) was conducted under anaerobic conditions by adding a consortium of the four strains that were positive in biosurfactant screening tests. The results showed 53% removal of n-alkane C9-C30 and a reduction in surface tension from 69 ± 0.5 mN/m to a minimum of 54.33 ± 0.5 mN/m. The results suggest the potential successful application of bioaugmentation for in situ biological treatment in the oil sands industry. Full article

Phosphogypsum (PG), a major by-product of the phosphate industry, has potential for improving acidic and nutrient-poor red soils, yet its agronomic benefits and heavy metal risks require systematic evaluation. A field experiment was conducted with five treatments, CK (soil only), GT (50% modified phosphogypsum, MPG), TT (40% MPG), ZT (50% phosphorite tailings), and DT (25% MPG + 25% lake sediment), to assess their effects on soil properties, enzyme activities, peanut growth, yield, quality, and heavy metal accumulation. All amendments improved soil structure, moisture retention, nutrient availability, and enzymatic activities. Peanut pod and kernel yields increased under all treatments, with DT achieving the greatest improvements (29.89% and 40.88%, respectively), whereas ZT showed the weakest response (1.91% and 6.26%). DT also achieved the highest soil quality index, and performed best in both yield improvement and root development. Although Cd accumulation increased under DT, heavy metal concentrations in peanut kernels remained below national food safety limits. Overall, DT was identified as the most effective amendment for enhancing red soil fertility and peanut productivity, while long-term monitoring of Cd bioavailability is recommended to ensure sustainable and safe application. Full article

Against the backdrop of accelerating climate change, extreme weather events have increasingly caused yield losses in agricultural crops. Meanwhile, they undermine the stability of production systems, posing an increasingly severe threat to agriculture. This study draws on the “diversity–stability” hypothesis to construct a country-level measure of agricultural production resilience in China (ARES). Using output time series for multiple agricultural products, we capture the co-movements of shocks and system resilience through output stability and volatility. By combining ARES with climate exposure measures, we assemble a panel dataset covering 1343 counties over the period 2000–2023 and employ a dynamic panel threshold model to jointly account for persistence in ARES and state-dependent nonlinearities in climate impacts. The results reveal significant path dependence in ARES and pronounced threshold effects across climate dimensions. In the full sample, extreme high-temperature days become significantly detrimental after crossing the threshold, whereas extreme low-temperature days become significantly beneficial in the high-exposure regime. Extreme rainfall days and extreme drought days generally exhibit positive effects that weaken markedly beyond their respective thresholds, indicating diminishing marginal gains in ARES under severe exposure. The comprehensive climate physical risk index significantly suppresses ARES when it is below the threshold value; however, after surpassing the threshold, its marginal effect becomes significantly weaker. Heterogeneity analyses across hilly, plain, and mountainous areas, as well as nationally designated key counties for poverty alleviation and development, further show that threshold locations and regime-specific effects differ substantially by terrain and development conditions. These findings highlight the need for “threshold-based” climate adaptation governance, emphasizing targeted investments and risk-financing instruments to prevent ARES collapse under tail-risk regimes. Full article

This study investigates the dynamic patterns of global financial risk transmission across 11 major economies and four key asset classes (stocks, bonds, foreign exchange, and gold) using daily data spanning 2012 to 2025. To capture the non-linearities of extreme market stress, we construct a multilayer directed network based on least absolute shrinkage and selection operator (LASSO) penalized quantile regression at the 5% lower tail. We estimate tail risk spillovers using a one-year rolling window approach and identify systemically important nodes via an extended PageRank algorithm applied to the resulting adjacency tensors. Empirical results suggest that the rankings of systemically important countries undergo significant re-orderings during crisis periods. We find robust statistical evidence that the Herfindahl–Hirschman Index (HHI) of risk concentration provides forward-looking information regarding structural polarization and systemic fragility. These observed associations remain consistent across alternative quantile thresholds, varying lag lengths, and alternative rolling window specifications. Our results provide granular insights for policymakers monitoring cross-asset contagion and provides a framework for institutional investors to assess potential tail-risk hedging strategies within an increasingly interconnected multilayer architecture. Full article

The alpine grassland ecosystem in the Source Region of the Yellow River (SRYR) faces the dual pressures of ecological protection and economic development. Its ecological fragility and climate sensitivity make local animal husbandry susceptible to meteorological disasters. To overcome adverse selection and moral hazard in traditional animal husbandry insurance, this study integrates 963 field sampling observation data, over 400 valid herdsmen survey data, and long-term environmental time series variables. A random forest model (R² = 0.59, RMSE = 65.84 g/m², superior to the artificial neural network in this paper) was used to estimate grass yield. Hodrick–Prescott (HP) filtering was used to separate meteorological yield per unit area and derive yield loss rate. A joint distribution model of meteorological indicators and loss rate was constructed using a Copula function to capture tail-dependent structures, providing a basis for determining trigger thresholds and actuarial pricing of pure insurance premiums. The study reveals the transmission mechanism of climate disasters to feeding costs and designs regional drought and snow disaster index insurance. The compensation standard is based on meteorological indicators falling below the trigger threshold and a yield reduction rate greater than 5%. Using 10,000 Monte Carlo simulations, the drought premium rates for zones I-IV are determined to be 2.03–6.03%, and the snow premium rates to be 2.25–5.42%, corresponding to a premium of RMB 5.21–9.61 per mu for drought and RMB 5.78–8.64 per mu for snow. This design reduces basis risk through zoning and composite triggering, providing a scientific tool for climate risk management in alpine grasslands. Full article