Search Results (196)

Faced with both global climate change and rapid urbanization, understanding how the built environment affects surface runoff is essential for strengthening urban hydrological resilience. However, elucidating the nonlinear and interactive effects of three-dimensional buildings and two-dimensional green spaces on surface runoff potential in urban blocks remains a scientific challenge for precise flood-mitigation spatial planning. Using six typical waterlogging-prone blocks in Shenyang as case studies, this study constructs a morphological index system for urban gray–green spaces and reveals the nonlinear effects of each index on surface runoff potential using an interpretable Random Forest (RF)–SHAP model. The results indicate that the RF model reliably captures the complex spatial patterns of simulated local surface runoff potential (R² = 0.723–0.825). At the block scale, the surface runoff response exhibits a dual character: it is predictively dominated by three-dimensional morphological dominance and two-dimensional base regulation. Three-dimensional building morphology generally demonstrates pronounced unidirectional thresholds and high-value saturation in model prediction. In particular, when the core indicator, building spatial congestion degree (B_SCD), crosses a critical threshold, surface runoff potential rises sharply. The coefficient of variation in building height (B_HVC) shows a “V-shaped” reversal in areas of extreme surface runoff potential, whereas two-dimensional green space indicators display clear asymmetric critical points. Significant reductions in surface runoff potential appear only when green space scale (G_LPI), boundary complexity (G_LSI), or fragmentation (G_PD) exceed specific model-identified thresholds. Furthermore, the study demonstrates marked interactive effects between the morphologies of gray–green spaces. High surface runoff risk from dense, large buildings can be substantially offset by large green space patches (G_LPI) with highly complex boundaries (G_LSI). The advantage of vertically staggered buildings (B_HVC) requires a green space base with low fragmentation (G_PD) to realize a gray–green synergistic mitigating effect. These findings provide theoretical and methodological support for enhancing the hydrological resilience of urban blocks. Full article

Slow reaction kinetics of the hydrogen oxidation reaction (HOR) and hydrogen evolution reaction (HER) under alkaline conditions limits the performance of anion exchange membrane fuel cells and water electrolysers (AEMFC and AEMWE). Consequently, high loadings of PGM metal-based compounds such as Pd-CeO₂ and PtRu are required to obtain competitive performance. The amount of precious metals present can be reduced by exploiting interaction with an active support material that tunes both hydrogen desorption and hydroxyl adsorption, processes that are key descriptors of HOR activity. In this work, NiWO_3−xC is prepared, composed of oxygen-deficient tungsten oxide (WO_3−x) doped with Ni nanoparticles and mixed with conductive carbon (50:50 wt%). This material is decorated with Pd nanoparticles (6.6 wt% Pd loading). Structural analysis (XRD, XPS, and HR-TEM/STEM) confirm a hybrid morphology of Pd nanoparticles deposited on both the Ni and W portions of the support. The HOR and HER activity was studied using electrochemical tests and compared to the performance of both a Pd/C standard with equivalent Pd loading (6.9 wt%) and the NiWO_3−xC support. The Pd-normalized exchange current densities for the HOR (I⁰) are 18.7 A g_Pd⁻¹ for Pd/NiWO_3−xC and 3.21 A g⁻¹ for Pd/C. The enhanced HOR activity of Pd/NiWO_3−xC translates to high power densities in AEM fuel cell tests with this catalyst applied to the anode electrode (up to 0.9 W cm⁻²). Full article

Satellite-based precipitation products such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG, V07) provide global coverage but exhibit systematic biases in daily accumulations, particularly for extreme events. This study presents a hybrid bias-correction framework (LSEQM+DL) for daily satellite precipitation that sequentially integrates Linear Scaling (LS) for mean bias, Empirical Quantile Mapping (EQM) with a Generalized Pareto Distribution (GPD) tail adjustment for distributional alignment, and a Convolutional Neural Network (CNN) refinement that targets extreme-precipitation pixels. A station-density confidence mask scales the deep-learning influence with gauge density, so the CNN refinement is strongest where the reference, the CPC Unified Gauge-Based Analysis of Daily Precipitation (CPC-UNI), is best constrained. The framework targets the IMERG Late Run (IMERG-L), whose roughly 14 h latency suits near-real-time flood monitoring. It is applied over Indonesia (2001–2025) and evaluated against CPC-UNI and 171 independent stations of the Meteorological, Climatological, and Geophysical Agency (BMKG) through three pillars: adjusting values, aligning distributions, and preserving extremes. At independent stations, the correction brings the standard deviation ratio from 0.71 (LS) to 1.00, the relative bias from $-$11.4% to $-$0.6%, and the 99th-percentile ratio from 0.71 to 1.01, and reduces a 21% over-estimation of wet-day frequency to within 5% of that observed. These gains carry a designed cost: the probability of detection falls from 0.78 to 0.65, while pixel-level temporal metrics (correlation, root-mean-square error, Nash–Sutcliffe efficiency) remain largely unchanged, confirming that the framework improves statistical properties rather than day-to-day timing. Relying only on globally available satellite and gauge-analysis data, and degrading gracefully where gauges are sparse, the framework is portable in principle with regional recalibration of its three tuning parameters. The corrected near-real-time product, with its station-density mask as a spatially explicit quality indicator, is intended to support flood monitoring, water resource management, and climate risk assessment in Indonesia and other gauge-sparse tropical regions. Full article

These notes comprise the material I presented during the International Workshop and School on Hadron Structure & Strong Interactions which took place in Nanjing in 2025. The aim of the lectures was to introduce and motivate the study of Generalised Parton Distributions (GPDs) for an audience of Master and Ph.D. students in hadron physics, with little to no background regarding the description of exclusive processes at high virtuality. Full article

Psoriasis is a chronic inflammatory autoimmune skin disease for which no standardised and reliable molecular biomarkers of disease course or activity are currently available. Here, we aimed to identify serum biomarkers of psoriasis. Serum samples from 40 patients with psoriasis and 40 healthy volunteers were analysed using ELISA and Proximity Extension Assay proteomics. ELISA revealed significantly increased serum levels of AGO2 and APOA1 in psoriatic patients versus controls, with a strong association between APOA1 and psoriasis (OR = 20.72, 95% CI of 4.57–93.87, p = 0.000137). Targeted serum proteomics additionally identified 35 differentially expressed proteins, including well-known psoriasis drivers (e.g., top upregulated IL17A and SERPINB4). The most downregulated was adrenomedullin (ADM, FC = −10.12). For 14 altered proteins, no previous direct associations with psoriasis were reported. Among them, DEFB103A_DEFB103B and DSG3 showed the best discrimination between psoriasis and control samples, while SERPINB4 correlated with psoriasis severity. APOA1, DEFB103A_DEFB103B, and DSG3 emerge as novel candidate circulating psoriasis biomarkers, and SERPINB4 as a biomarker of psoriasis severity. The functional role of DSG3 and other newly identified proteins (ACRV1, HAO1, ADH4, GPD1, GFER, PTGES2, DSG3, AFAP1L1, GALNT3, RASGRP2, MAP2K6, LXN, NBEAL2, and VPS54) in psoriasis requires further studies. Full article

Following the elucidation of the fungal bioluminescence pathway (FBP), it was quickly adopted as a reporter system in plants; however, no such application has been documented in fungi to date. In this study, we established for the first time a luminescent reporter in the commercially important mushroom Hypsizygus marmoreus by expressing the luciferase gene from the luminous fungus Neonothopanus nambi. Using an established Agrobacterium-mediated transformation method, we separately introduced the wild-type luciferase gene nnLuz and the previously reported optimized variant nnLuz-v4 that can enhance bioluminescence expression into H. marmoreus arthroconidia. Both genes were stably integrated into the genome and expressed under the control of the H. marmoreus Glycerol 3-phosphate dehydrogenase (GPD) gene promoter. Upon addition of exogenous luciferin, transformants carrying the wild-type nnLuz produced clear, readily detectable bioluminescence signals, whereas no luminescence was observed in untransformed controls. Unexpectedly, the wild-type luciferase consistently exhibited substantially higher luminescence intensity than the optimized nnLuz-v4 variant. This finding suggests that codon optimization may be unnecessary or even detrimental when the donor and host are phylogenetically close basidiomycetes. The successful deployment of the fungal luciferase gene in H. marmoreus provides a sensitive and non-invasive genetic tool that does not require external excitation. This system opens new avenues for promoter characterization, real-time gene expression monitoring during mushroom development, and molecular breeding efforts aimed at improving agronomically important traits. Full article

The Inner Mongolia reach of the Yellow River faces compound “low flow, high sediment” extremes under reservoir regulation, threatening flood and ice-flood safety in ways that traditional mean-based or correlation-based methods fail to quantify. This study integrates POT-GPD extreme value theory with a vine copula-CoVaR framework using daily data (1951–2023) from four stations. The financial CoVaR concept was adapted to rivers through three hydrological modifications: a 5-day hydrodynamic lag, redefinition of the baseline to the downstream unconditional VaR, and semi-parametric tail modeling. Bootstrap confidence intervals (n = 1000) and a sensitivity analysis to the upstream–downstream lag (τ = 3–7 days) and the period cutoff (1984–1990) were used to assess robustness. Bayangol exhibits the highest Expected Shortfall (ES₉₅ = 0.0329 kg·s·m⁻⁶). The Bayangol → Toudaoguai path is the only persistent positive risk transmission link, with ΔCoVaR showing a directionally consistent increase of 253% from the natural period (1951–1986) to the regulated period (1987–2023); by contrast, ΔCoVaR from Dengkou to Toudaoguai remains near zero or negative when assessed under the conventional bivariate framework. A three-dimensional vine copula analysis, conducted independently for the pre- and post-reservoir periods, reveals a qualitative reversal of compound extreme spillover that is masked when the two periods are pooled. While the bivariate analysis identifies Bayangol → Toudaoguai as the only persistent positive spillover route at the annual scale, the 3D vine analysis unpacks the compound extreme mechanism at the daily scale. Under the joint compound extreme condition (upstream Q and S each ≥ Q₉₀), the conditional VaR₉₅ of downstream sediment concentration shifts from systematically negative in P1 (ΔVaR₉₅ = −4.75 kg·m⁻³ at the 90th-percentile threshold, indicating natural attenuation) to systematically positive in P2′ (ΔVaR₉₅ = +4.70 kg·m⁻³, +86.9% relative increase, indicating amplification). The same reversal is observed for the tail mean (ΔES₉₅), is preserved across four compound extreme thresholds (Q₇₅–Q₉₀), and is robust to the choice of period cutoff (28/28 cases reverse across seven candidate cutoffs). Bidirectional counterfactual simulations indicate that the copula shift from tail independence (Clayton) to tail dependence (Gaussian) alone elevates extreme concurrence probability by 58% (from 2.21% to 3.49%), while marginal distribution changes contribute negligibly (≤0.1 percentage points). Structural deterioration of water–sediment coordination therefore dominates risk amplification. The copula-CoVaR framework offers a candidate tool that requires further validation with large samples for tail risk assessment in heavily regulated fluvial systems. Full article

In response to the challenges of large fruit-scale variation, dense target distribution, severe leaf occlusion, and complex backgrounds in green plum detection within orchards, this paper proposes a lightweight multi-scale edge geometry-aware network (MEGNet). First, the Green Plum Detection Dataset (GPD) is constructed to provide realistic orchard scene data for the task. Next, we enhance the model’s structure based on YOLO11n by designing an efficient multi-scale feature fusion attention module (EMFFA) to improve the expression of multi-scale fruit features. We also introduce a color-edge guided dual-discriminator feature enhancement module (CED) to strengthen feature discrimination in complex backgrounds. A coordinate attention ghost detection head (CAGDetect) is proposed to reduce model parameters and computational complexity. Additionally, a geometry-consistency modulated CIoU loss function (GC-CIoU) is introduced to improve target localization stability in occluded and dense scenes by incorporating a geometric consistency modulation mechanism. Experimental results show that on the GPD, MEGNet achieves a Precision of 93.9%, Recall of 86.2%, mAP₅₀ of 93.2%, and mAP_50:95 of 76.1%. The model’s Parameters are only 2.13 M, with FLOPs of 4.7 G. Compared to the baseline YOLO11n model, Precision, Recall, mAP₅₀, and mAP_50:95 are improved by 2.5%, 5.2%, 4.4%, and 4.6%, respectively. Additionally, deployment experiments on the Jetson Orin Nano embedded device demonstrate real-time detection speeds of 31–33 FPS. The proposed method provides an efficient and reliable solution for intelligent harvesting systems, orchard monitoring platforms, and agricultural robot vision perception. Full article

The escalating frequency and intensity of extreme rainfall events driven by climate change threaten infrastructure resilience and societal safety, underscoring the urgent need for robust models to predict these events. Previous studies on the integration of Extreme Value Theory (EVT) and machine learning in modelling extreme rainfall events have not explored the use of a time-varying threshold. This study introduces a novel time-varying threshold Generalised Pareto (GP) regression tree for modelling extreme rainfall in Durban, South Africa. The proposed hybrid model combines EVT with covariate-driven regression tree partitioning, allowing the threshold to evolve dynamically with meteorological conditions. Using daily rainfall and meteorological covariate data from 1981 to 2025, the model was developed, pruned, and benchmarked against a static-threshold GP regression tree and a time-varying threshold Generalised Pareto Distribution (GPD). Evaluation based on the Bayesian Information Criterion (BIC) and log-likelihood demonstrated the superior performance of the proposed model in capturing covariate-driven heterogeneity and temporal variability of rainfall extremes. Four distinct climatic regimes with different tail behaviours and return levels were identified. This study provides the first meteorological application of a time-varying threshold GP regression tree and offers practical insights into flood risk assessment and climate resilience planning in the city of Durban. Full article

To mitigate the gas–liquid mass-transfer bottleneck in anion-exchange membrane water electrolysis (AEMWE), a 3D multiphysics numerical model was developed to systematically investigate the regulatory effects of gradient porosity (GPD) and gradient pore-size distribution (GPSD) on anode reaction kinetics and cell polarization. Single-factor analysis reveals that increasing the GPD/GPSD from the membrane side toward the flow channel side effectively reduces activation overpotential due to the high specific surface area of small pores near the membrane, while simultaneously lowering mass-transfer resistance through high porosity and large pores near the flow channel. Conversely, a decreasing gradient leads to localized gas stagnation and uneven mass transfer, deteriorating cell performance. Furthermore, an innovative synergistic design is proposed featuring a simultaneous linear increase in porosity (0.6 to 0.9) and pore diameter (0.11 to 0.17 mm). This configuration achieves a cell voltage of only 1.812 V at 1100 mA/cm² (1 mol/L KOH, 80 °C), approximately 40 mV lower than that of conventional uniform structures, thereby significantly reducing energy consumption at high current densities. This study provides a mechanistic framework for the precise architectural design of high-performance AEMWE electrodes, highlighting the importance of spatial heterogeneity in optimizing two-phase transport. Full article

Background/Objectives: Pest management strategies rely on insecticides such as deltamethrin (DM), a commonly applied type II pyrethroid. As a natural component of food-associated microflora, Saccharomyces cerevisiae inevitably encounters DM residues in crops used for fermentation processes, including dough leavening and winemaking. However, the prolonged effect of DM exposure on yeast fermentation performance and its capacity to remove DM remained unclear. Methods: In this study, S. cerevisiae was continuously exposed to a non-lethal concentration (10 mg/L) and a low-inhibition toxic concentration (30 mg/L) of DM for 30 days. Results: Yeast exhibited high removal capacity, removing 98.05 ± 1.2% and 98.28 ± 0.4% of DM at 10 mg/L and 30 mg/L, respectively. Prolonged exposure to DM at both concentrations did not significantly affect biomass formation, glucose consumption, ethanol production, or acetic acid levels. In contrast, glycerol production increased markedly, reaching 1.1 g/L and 1.5 g/L in cultures exposed to 10 mg/L and 30 mg/L DM, respectively. Consistent with these changes, the expression levels of GPD1 and GPD2, which encode rate-limiting enzymes in glycerol biosynthesis, were upregulated in a dose-dependent manner. Conclusions: Given the fact that Saccharomyces cerevisiae is a workhorse for the biotechnological industry and has a wide range of applications, including in the food industry, elevated glycerol production in yeast under DM exposure is noteworthy in terms of yeast-based applications. 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

Extreme sea levels along the Mexican coasts pose an increasing risk to coastal infrastructure and communities, particularly under the combined influence of tropical cyclones and ongoing sea-level rise. This study analyzes tide-gauge records from the Mexican Pacific and Gulf of Mexico–Caribbean coasts to characterize the statistical behavior and seasonal modulation of extreme sea-level residuals. Astronomical tides were removed through harmonic analysis to isolate the meteorological residual associated with storm-driven processes. Extreme events were evaluated using complementary extreme-value frameworks, including Generalized Extreme Value (GEV) distributions applied to monthly maxima and a Peaks-Over-Threshold (POT) approach applied to the continuous residual series with temporal declustering and Generalized Pareto Distribution (GPD) fitting. While both approaches consistently capture regional patterns, the POT–GPD framework is adopted as the primary basis for return-level estimation due to its explicit representation of event-scale extremes. The results reveal marked regional variability. Pacific stations exhibit bounded or near-Gumbel behavior (ξ ≈ −0.30 to −0.02) and a strong seasonal concentration of extremes during the tropical cyclone season. In contrast, Gulf of Mexico–Caribbean stations display higher absolute extremes and a broader seasonal footprint, with Veracruz showing a tendency toward heavier-tailed behavior (ξ ≈ 0.13). Return levels for a 25-year return period range from approximately 0.85–0.95 m in the Pacific to about 1.7 m in Veracruz. Longer return periods (e.g., 100 years) exceed 2.2 m in Veracruz but are associated with substantial uncertainty due to record-length limitations. The analysis of ENSO variability indicates that ENSO acts primarily as a secondary modulator of background sea-level variability rather than a deterministic driver of extreme events, with the largest anomalies typically associated with tropical cyclone activity. Overall, the results demonstrate that extreme sea levels along the Mexican coasts are governed by region-specific forcing and tail behavior requiring localized extreme-value modeling strategies. The proposed framework provides a robust and reproducible baseline for coastal hazard assessment and supports the integration of sea-level rise into future risk and design analyses. Full article

Drunken horse grass (Achnatherum inebrians) plays a vital role in ecological restoration and grassland sustainability in Northwest China, but its ecological functions are increasingly threatened by emerging fungal diseases. In 2024, a leaf spot disease characterized by brown lesions with yellow halos was observed on drunken horse grass in Gansu Province, China. The causal pathogens were identified as Alternaria alternata and Alternaria infectoria based on morphological characterization, pathogenicity tests, and multi-locus phylogenetic analysis (ITS, TEF, GPD, RPB2, Alt a 1, endoPG, and OPA10-2). Preliminary fungicide sensitivity assays revealed that tetramycin and difenoconazole had the strongest inhibitory effects against mycelial growth in vitro. The EC₅₀ values for tetramycin were 0.0755 mg/L (A. alternata) and 0.2175 mg/L (A. infectoria), while for difenoconazole, they were 0.1023 mg/L (A. alternata) and 0.0599 mg/L (A. infectoria). To our knowledge, this is the first report of Alternaria species infecting the host plant, drunken horse grass, providing an essential basis for the effective management of this disease and the protection of grassland ecosystems. Full article

Sparassis latifolia is an edible and medicinal mushroom with significant economic value, now commercially cultivated on a large scale in China. However, current cultivars face challenges, including an extended mycelial growth period and unstable fruiting body yields. Advances in molecular breeding and functional genomics for this species are hindered by the absence of a reliable genetic transformation system. In this study, we first determined that S. latifolia is highly sensitive to carboxin and hygromycin, two selective agents commonly used in fungal genetics. We subsequently constructed a novel binary vector, pCbxHyg, harboring a carboxin resistance cassette driven by its native Pleurotus eryngii promoter and a hygromycin resistance cassette under the control of the P. eryngii Glycerol 3-phosphate dehydrogenase (GPD) gene promoter. Initial transformation attempts using Agrobacterium-mediated transformation of liquid-cultured mycelial pellets were unsuccessful. During microscopic examination, we discovered that S. latifolia mycelia produce abundant asexual chlamydospores. Using these chlamydospores as recipient material, we efficiently and reproducibly obtained transformants with the pCbxHyg vector under both carboxin and hygromycin selection. This method highlights the advantage of using asexual spores of Basidiomycetes as recipients for genetic transformation. PCR analysis confirmed the stable integration of the exogenous resistance genes into the fungal genome. The functionality of the system was further validated by transforming chlamydospores with a vector carrying a β-glucuronidase (GUS) reporter gene, whose expression was confirmed via histochemical staining of the resulting transformant mycelia. This work establishes the first successful Agrobacterium-mediated genetic transformation system for S. latifolia, providing a foundational platform for future gene function studies and molecular breeding efforts. Full article