Search Results (108)

This study adapted and applied a spatially distributed analytical model to estimate the annual representative leached fraction and the annual potential leached mass of atrazine in the Cauquenes catchment in Chile under contrasting Mediterranean hydroclimatic conditions. The model was based on van der Zee and Boesten and Rakonjac et al. and was modified to account for the strong seasonality of precipitation and evapotranspiration by using representative daily hydrological conditions derived from monthly averages. Spatially distributed soil, climate, land-cover, and atrazine application data were integrated at the pixel scale, including locally corrected soil organic carbon, hydraulic properties, precipitation, evapotranspiration, leaf area index, and annual atrazine dose. The model was applied to two contrasting years, 2018 and 2023, and outputs were aggregated at the pixel, land-cover, hotspot, and catchment scales. The results showed a marked hydroclimatic control on potential atrazine leaching. In the drier year, 2018, both the annual representative leached fraction and the annual potential leached mass were generally very low across the catchment, whereas in the wetter year, 2023, moderate-to-high leaching values became much more spatially extensive, and hotspot areas expanded substantially. At the catchment scale, potential leached mass increased from 0.088 kg in 2018 to 179.784 kg in 2023, while the percentage of applied mass potentially leached increased from 5.50 × 10⁻⁵% to 0.112%. Land-cover classes influenced the results both through the spatial allocation of atrazine application and through LAI-dependent partitioning of evapotranspiration. Global sensitivity analysis using the Morris method identified K_OC and DT50 as the dominant controls on annual potential leached mass, and spatial uncertainty propagation was performed. Overall, the proposed framework provides a potential annual screening estimate and may serve as a preliminary screening tool to prioritize areas for targeted monitoring and future model benchmarking in Chile. Full article

Background: Therapeutic resistance following cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) plus endocrine therapy (ET) represents a key unmet need in hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2−) metastatic breast cancer (mBC). Treatment paradigms have advanced from non-targeted options, such as fulvestrant monotherapy or everolimus-based combinations, to precision medicine strategies, including inhibitors of the PI3K/AKT pathway, oral selective estrogen receptor degraders (SERDs), and novel ER-modulating agents, often guided by biomarkers and molecular surveillance. Methods: This narrative review synthesizes evidence from randomized clinical trials, real-world studies, and biomarker-driven analyses published from 2010 to 2026, with emphasis on next-generation sequencing (NGS)-guided genomic profiling, targeted pathway therapies, and circulating tumor DNA (ctDNA)-based proactive interventions in the post-CDK4/6i setting. This review was conducted and reported in accordance with the SANRA recommendations for narrative reviews. Results: Early second-line standards, including fulvestrant and alpelisib for PIK3CA-mutated tumors, established the basis for biomarker-guided treatment in hormone receptor–positive, HER2-negative metastatic breast cancer. With the widespread use of CDK4/6 inhibitors in the first-line setting, the optimal post-progression strategy has shifted toward molecularly selected combination approaches rather than single-agent endocrine therapy, as endocrine monotherapy has shown limited efficacy in acquired resistance. Multiple randomized studies have demonstrated that adding targeted agents to endocrine therapy improves progression-free survival compared with hormonal therapy alone, supporting combination regimens as the preferred strategy after CDK4/6 inhibitor progression, except in carefully selected patients with low disease burden, indolent biology, or frailty where tolerability is a major concern. Precision-based trials have further refined this approach. Elacestrant improved progression-free survival in ESR1-mutated disease in the EMERALD trial, capivasertib plus fulvestrant demonstrated significant benefit in tumors harboring AKT/PIK3CA/PTEN pathway alterations in CAPItello-291, and inavolisib plus palbociclib and fulvestrant achieved both progression-free and overall survival improvement in PIK3CA-mutated patients with early relapse in INAVO120. Real-world analyses further support the effectiveness of these biomarker-directed strategies across diverse clinical subgroups. Comprehensive genomic profiling has identified multiple resistance mechanisms, including ESR1 mutations, PI3K/AKT/mTOR pathway activation, RB1 loss, and FGFR alterations, which may co-occur and reduce sensitivity to endocrine monotherapy. While ESR1 and PI3K pathway alterations now guide approved therapies, FGFR alterations remain investigational targets, with ongoing trials evaluating selective FGFR inhibitors. Proactive switching approaches evaluated in SERENA-6 and PADA-1 demonstrate that serial circulating tumor DNA (ctDNA) monitoring can detect emergent ESR1 mutations before radiographic progression, providing a clinically actionable lead time for early therapeutic modification and extending endocrine-based disease control by approximately 5 to 7 months. Conclusions: Post-CDK4/6i management increasingly relies on NGS-guided precision approaches, integrating pathway-specific therapies and ctDNA surveillance to tailor sequencing based on resistance profiles, prior ET response, and tumor heterogeneity. Future investigations into novel ER degraders and multi-targeted combinations hold potential to further optimize algorithms, extend non-chemotherapy options, and enhance survival in HR+/HER2− mBC. Full article

Accurate and timely monitoring of soil salinity is of great significance for the ecological restoration of saline-alkali land and precision agricultural management. In this study, a typical coastal saline-alkali farmland located in Huanghua City, Hebei Province, China, in the Bohai coastal region, was selected as the study area. High-resolution images were acquired using an unmanned aerial vehicle (UAV) equipped with a multispectral sensor, and ground soil salinity samples were collected synchronously. Based on the construction of a feature library comprising spectral reflectance, vegetation indices, and salinity indices, three algorithms, PSO-SFLA, MultiSURF, and VIP, were employed for feature selection. Subsequently, an ensemble model was established, utilizing Ridge Regression (Ridge), Random Forest (RF), and Extra Trees (ET) as primary base learners, and Extreme Gradient Boosting (XGBoost) as the secondary meta-learner. This ensemble model was applied for soil salinity inversion. Furthermore, the coefficient of determination (R²), standardized root mean square error (S_RMSE), and the ratio of performance to interquartile distance (RPIQ) were introduced to comprehensively evaluate the accuracy of the models. Finally, the intrinsic physical responses of the features were explored through SHAP. The results showed that the optimization by the PSO-SFLA effectively reduced the impact of spectral multicollinearity, and 11 core features highly sensitive to salinity were selected from a vast number of indices. The ensemble model showed better predictive performance on the independent test set, achieving an R² of 0.758, an S_RMSE of 0.285, and an RPIQ of 3.382, outperforming the single Ridge, RF, and ET models under the current experimental conditions. Based on this model, the spatial distribution map of soil salinity in the experimental area was generated. The integrated and interpretable workflow proposed in this study, combining UAV multispectral imagery, PSO-SFLA-based feature selection, ensemble learning, and SHAP interpretation, provides a practical approach for accurate soil salinity inversion and dynamic agricultural monitoring in coastal saline-alkali lands. Full article

Mediterranean coastal groundwater-dependent ecosystems are among the most vulnerable environments to the combined effects of climate change and local anthropogenic pressures, yet long-term quantitative assessments disentangling these drivers remain limited. The 41-year hydro-ecological dynamics (1984–2025) of “Le Soglitelle”, a transitional man-made coastal GDE located in the Campania Plain (southern Italy), were reconstructed across three management regimes: illegal hunting via electric pumps augmentation of flooded areas (1984–2004), post-seizure transition (2005–2015), and fenced natural reserve sustained by artesian wells flow (2016–2025). A monthly multi-sensor time series of seven spectral indices was derived from cross-calibrated Landsat program Surface Reflectance products via Google Earth Engine. Spectral indices were then combined with climatic variables (precipitation, reference evapotranspiration, air temperature) and then integrated in a statistical framework including Mann–Kendall test, Pettitt test, and Principal Component Analysis. Significant breakpoints were identified for the water fraction (2007; mean decrease from 0.18 to 0.09) and the Normalized Difference Vegetation Index (2009; mean increase from 0.30 to 0.42), consistent with a hydrological regime shift following the interruption of anthropogenic pressures. The relationship between the water fraction and the Vegetation Soil Salinity Index was 2.7 times steeper in the last period than the first one, indicating that, for an equivalent flooded extent, osmotic stress on vegetation is substantially higher under the artesian flow alone, likely due to reduced dilution of saline inputs combined with the effect of ongoing climate change. PCA showed that PC1 reflected the transition from anthropogenic to more natural system conditions, whereas PC2 was associated with increasing ET₀, became more prominent during the last period of management, suggesting a shift toward stronger climate-driven control. Long-term satellite monitoring provides a quantitative baseline for designing targeted management interventions aimed at sustaining ecosystem functioning under ongoing Mediterranean warming. Full article

Urban drought pressure is increasing the operational risk and cost of maintaining municipal green infrastructure. Irrigation is still widely managed through fixed routines and fragmented information. To address this challenge, the study develops an integrated operational analysis by combining water consumption records, maintenance data and a GIS inventory for twenty municipal green spaces. System characterisation and performance screening were carried out using hourly meter readings to distinguish typical scheduled irrigation peaks from non-standard consumption patterns. To move from monitoring to control, irrigation needs were estimated using evapotranspiration (ET₀) and a garden-coefficient logic adapted to urban planting conditions and compared with measured consumption. The comparison indicates a potential reduction of 29–61% through improved scheduling and system adjustment. Based on the diagnosis, technical intervention scenarios were defined and assessed using techno-economic metrics, including ground-cover redesign and Mediterranean-adapted planting strategies. To support implementation, options were organised into intervention priorities using a multicriteria tool that balances water savings, costs and feasibility under municipal operations. Coimbra, Portugal is used as a case study, and a pilot application in a city garden, supported by 797 user surveys, clarifies practical constraints for scaling beyond isolated pilots. Turf-free scenarios indicate a 53.4% reduction in water use and a 60.5% reduction in operational costs, with a payback period below three years. The results highlight the potential of data-driven irrigation management to support more resilient, cost-effective and water-efficient municipal green infrastructure across diverse urban contexts. Full article

Financial reporting integrity (FRI) plays a critical role in capital market efficiency, yet its determinants remain difficult to model due to nonlinear relationships, heterogeneous firm characteristics, and institutional differences across emerging markets. Prior research largely relies on linear econometric approaches, which may overlook threshold effects and complex governance dynamics. This study develops an explainable Optuna-optimized Extremely randomized trees (ET) ensemble framework to examine the association between controlling shareholders and FRI in Egypt and Saudi Arabia. Using a panel dataset of 1746 firm-year observations over the period 2014–2022, the model incorporates advanced preprocessing and mutual information-based feature selection to enhance predictive accuracy and robustness. The proposed model significantly outperforms regularized linear models, standalone machine learning models, and alternative ensemble techniques, achieving R² values of 0.7935 in Egypt and 0.9231 in Saudi Arabia, alongside substantial reductions in RMSE and MAE. Diebold–Mariano tests confirm that these performance gains are statistically significant (p < 0.01). Explainability analysis using SHAP reveals that firm size and market share are the dominant drivers of FRI, while blockholder ownership exhibits a nonlinear and context-dependent association. Partial dependence results show a complex, non-monotonic relationship in Egypt—consistent with a monitoring–entrenchment trade-off—contrasted with a predominantly positive and monotonic association in Saudi Arabia. Importantly, these nonlinear patterns are not detected in conventional panel fixed effects models, highlighting the limitations of standard econometric specifications in capturing complex ownership dynamics. The findings highlight the importance of institutional context in shaping governance outcomes and demonstrate how explainable ensemble learning can uncover hidden nonlinearities in financial reporting behavior. This study contributes by identifying nonlinear thresholds and cross-country variation in ownership effects while integrating predictive performance with interpretability, offering a robust framework for analyzing corporate governance mechanisms in emerging markets and supporting more informed decision-making by investors, regulators, and policymakers. Full article

Trees in arid mountainous forests adapt to seasonal water variability through dynamic eco-physiological adjustments. This study investigated the spatiotemporal dynamics and environmental drivers of such adaptations in Picea schrenkiana Fisch. et Mey, a keystone conifer in China’s Tianshan Mountains. We monitored key indicators—including osmoregulatory substances, antioxidant enzyme activities, and stoichiometric traits—across three regions (eastern, central, western) and three seasons (spring, summer, autumn) during the 2023 growing season. The results revealed significant seasonal shifts in all the measured traits (p < 0.05). Spring was characterized by high carbon allocation toward soluble sugars and starch, supporting growth; summer triggered elevated antioxidant enzyme activities to mitigate oxidative stress; and autumn favored nitrogen accumulation and proline synthesis, indicating preparatory storage for winter. Soil factors were primarily positively associated with antioxidant enzyme activity (path coefficient = 0.51; p < 0.001), whereas microenvironmental factors were more complex and often negatively correlated. The partial least squares path model confirmed that osmoregulatory substances centrally link stoichiometric adjustments with antioxidant defense, revealing an integrated physiological strategy. These findings elucidate the mechanism underlying the resilience of P. schrenkiana in arid highlands and provide a framework for its conservation under environmental change. Full article

As water resources are becoming increasingly scarce in the North China Plain, irrigation strategies that simultaneously improve grain yield and reduce irrigation water input are needed for winter wheat (Triticum aestivum L.) production. Current irrigation decision rules are based either on fixed soil moisture thresholds or on evapotranspiration (ET)-based ratios applied uniformly across the growing season, limiting their flexibility for growth stage-specific irrigation management. In this study, a multi-objective simulation optimization framework was developed to jointly optimize soil moisture lower control limits (irrigation trigger thresholds) and evapotranspiration-based irrigation replenishment ratios across key winter wheat growth stages. The framework integrated the AquaCrop-OSPy crop model with the PyFAO56 soil moisture balance, irrigation scheduling model and the NSGA-II evolutionary optimization algorithm. A field experiment was conducted during the 2024–2025 growing season in Laoling City, Shandong Province, China, employing a four-dense–one-sparse strip cropping pattern with two irrigation treatments: T1 (subsurface sprinkler irrigation) and T2 (shallow subsurface drip irrigation). The AquaCrop-OSPy model was calibrated and validated using measured canopy cover, aboveground biomass, grain yield, and soil moisture content in the 0–60 cm soil layer. Simulated canopy cover and grain yield showed good agreement with observations, with the coefficient of determination (R²) ranging from 0.87 to 0.94. For grain yield, the normalized root mean square error (NRMSE) ranged from 2.24% to 3.75%, and the root mean square error (RMSE) ranged from 0.29 to 0.54 t·ha⁻¹. For aboveground biomass, R² was 0.99, while RMSE ranged from 1.02 to 1.11 t·ha⁻¹, and NRMSE ranged from 14.25% to 15.49%. The PyFAO56 irrigation strategy model simulated average root-zone soil-moisture dynamics with satisfactory accuracy, with an R² of 0.86 and an RMSE of 5%. Multi-objective optimization (maximizing yield while minimizing irrigation volume) generated 23 Pareto-optimal irrigation strategies, with irrigation volumes ranging from 51 to 128 mm, corresponding yields ranging from 9.8 to 10.8 t·ha⁻¹, and irrigation water use efficiency (IWUE) ranging from 0.08 to 0.19 t·ha⁻¹·mm⁻¹. Correlation analysis within the Pareto set indicated that soil-moisture control lower limits during the regreening–jointing stage and higher soil-moisture control lower limits during the flowering–maturity stage were key controlling factors for achieving high yields and irrigation water use efficiency. The Entropy-Weighted Ranked Minimum Distance method identified an optimal irrigation scheme involving two irrigations (one at the end of the jointing stage and another at the beginning of the grain filling stage) involving an irrigation depth of 75 mm, achieving a simulated yield of 10.4 t·ha⁻¹ and an IWUE of 0.16 t·ha⁻¹·mm⁻¹. The proposed AquaCrop-PyFAO56-NSGA-II framework provides a flexible, process-based workflow for jointly optimizing irrigation control thresholds and evapotranspiration-based irrigation replenishment ratios across different winter wheat growth stages. Under the monitored conditions of the 2024–2025 wet season, the framework identified a two-irrigation strategy that balanced grain yield and irrigation input. This study should, therefore, be regarded as a proof-of-concept evaluation conducted in a well-instrumented single-site field setting rather than as a universally transferable recommendation. Because model calibration, within-season validation, and optimization were all based on one wet growing season at one site, the derived stage-specific thresholds, Pareto front, and S5 recommendation are most applicable to hydro-climatic conditions similar to the study year and should be further tested across contrasting year-types and locations before broader extrapolation. Full article

Healthcare Wireless Sensor Networks (HWSNs) have attracted significant attention due to their vital role in diseases’ diagnosis, monitoring, and treatment. By continuously collecting patients’ physiological data and enabling remote medical services, these networks can greatly improve the quality of healthcare. However, the inadequate handling of security and privacy issues poses serious risks to patients. In this context, signcryption schemes are essential cryptographic primitives that simultaneously provide authentication, confidentiality, and data integrity with a low overhead. Recently, Deng et al. proposed a certificateless signcryption (CL-SC) scheme for HWSNs and proved its security in the standard model. In this paper, we demonstrate that their scheme is insecure under an enhanced adversarial model, where a super Type II adversary, which is a malicious key generation center, can replace the system’s master public key using the master secret key under its control, and subsequently forge valid signcryptions on arbitrary messages on behalf of a sensor node. To address this vulnerability, we propose an enhanced CL-SC scheme based on elliptic curve cryptography (ECC). Under the hardness assumptions of the Elliptic Curve Decisional Diffie–Hellman Problem (ECDDHP) and the Computation Attack Algorithm (CAA), the proposed scheme achieves confidentiality and existential unforgeability against both super Type I and super Type II adversaries in the standard model. Performance analysis further shows that our scheme is efficient and well suited for resource-constrained HWSN environments. Full article

Despite the well-documented acceleration of climate-related economic losses in Europe, existing research has largely treated these damages as isolated stochastic events rather than as structurally embedded fiscal risks. This gap leaves EU fiscal governance frameworks inadequately prepared for the persistent, spatially concentrated, and temporally dependent nature of such losses. This study addresses this gap by investigating the systemic transformation of climate-related economic risks within the European Union, arguing that climate losses have evolved from unpredictable stochastic shocks into a persistent, structural burden on the European economy. Adopting a comprehensive multi-methodological approach, the research quantifies this transition by integrating spatial concentration metrics (HHI), advanced time-series modelling (OLS, ARIMA, ETS), and anomaly detection techniques to analyse loss patterns across the EU-27 from 1980 to 2023. The empirical results demonstrate three critical systemic dimensions: (1) a statistically significant upward shift in the baseline of economic damages; (2) a high geographical concentration of losses, with Germany, Italy, and France consistently bearing the largest share of climate-driven fiscal pressure; and (3) the emergence of volatility clustering, indicating that climate risks are becoming increasingly non-linear and embedded in the macroeconomic environment. The study contributes to the literature by reframing climate-related economic losses as a systemic fiscal phenomenon requiring structural governance reform, rather than ad hoc disaster response. The findings suggest that existing reactive policy frameworks are insufficient to address the scale of these structural risks. Consequently, the paper advocates for a paradigm shift in EU climate policy—moving toward anticipatory fiscal instruments, harmonised resilience financing, and monitoring systems designed to mitigate systemic volatility and cross-country economic asymmetry rather than merely responding to isolated disaster events. Full article

Precise estimation of evapotranspiration (ET) is essential for sustainable water management in arid agroecosystems, particularly for high-water-demand crops such as rice. This study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season. A network of 24 drainage lysimeters provided an independent observational benchmark (ET_lys); to represent the treatment-level response, lysimeter observations were aggregated as the mean across the 24 units for each UAV campaign. Thirteen UAV surveys supplied radiometric surface temperature and biophysical inputs (e.g., NDVI and fractional cover) to derive spatially explicit ET, while AquaCrop provided continuous daily simulations between flight dates. Direct lysimeter-based validation indicated high agreement for AquaCrop (R² = 0.85; RMSE = 0.26 mm d⁻¹; MBE = 0.01 mm d⁻¹) and moderate agreement for UAV–TSEB (R² = 0.66; RMSE = 0.81 mm d⁻¹; MBE = 1.01 mm d⁻¹). Model intercomparison further showed consistent temporal dynamics of ET (R² = 0.70; RMSE = 1.35 mm d⁻¹) and robust partitioning of crop transpiration (R² = 0.79; RMSE = 0.99 mm d⁻¹) and soil evaporation (R² = 0.76; RMSE = 1.03 mm d⁻¹) while revealing a systematic divergence under near-complete canopy cover: AquaCrop tended to suppress evaporation, whereas UAV–TSEB detected residual evaporation from the flooded surface. Overall, the results highlight the complementarity of both approaches—UAV–TSEB as a spatial diagnostic tool and AquaCrop as a temporally continuous simulator—providing a robust framework for ET monitoring, flux partitioning, and water-use-efficiency assessment in water-scarce rice systems. Full article

Surface soil moisture (SSM) serves as a critical indicator for regional water cycles, agricultural management, and drought monitoring. However, existing the SMAP data suffers from limited spatial resolution, making it challenging to meet the demands of large-scale, high-resolution applications. Taking Henan Province, located in east-central China with a continental monsoon climate and marked seasonal variability, as the study area, this research integrates multi-source data to develop a seasonal modeling strategy. Based on stacking ensemble learning, the SSM downscaling inversion model (SOBLE-Top5) is constructed. SHAP value attribution analysis is employed to reveal the primary drivers of seasonal dynamics. The results indicate: (1) The SSM exhibits distinct seasonal characteristics. Compared to the all-season modeling, the RMSE and R² metrics significantly improve during spring and summer. The winter ET and RF models show an approximately 9–14% higher R² and a 47–50% lower RMSE. (2) The SOBLE-Top5 strategy achieved up to a 4.65% higher R² and a 21.22% lower RMSE compared to the optimal single base model. (3) Spatial variations in the SSM characteristics reveal stable performance during the winter. The spring saw slight SSM declines in the northern regions due to rising temperatures. The study area reached its annual low (<0.08 m³/m³) in May–June. Driven by flood season precipitation, July–August witnessed local increases exceeding 52%. The autumn exhibited a stable-then-rising trend with pronounced north–south gradient characteristics. (4) The SHAP analysis indicates that the winter SSM is primarily controlled by bulk density and clay content. The spring SSM is most influenced by LST, followed by bulk density. The summer and the autumn SSM are synergistically driven by multiple factors including elevation, temperature, and precipitation, with the summer precipitation exerting the most significant impact on instantaneous SSM variations. Full article

African vegetation responses to extreme drought represent a key challenge for global change research and sustainable water–land resource management. Satellite remote sensing provides long-term observations of vegetation dynamics, yet conventional analyses focus on vegetation structural, greenness, or productivity changes, lacking of understanding on physiological adaptation. This study applies a multi-model framework integrating high-temporal-resolution (4-day) and multi-spectral satellite data with machine learning to disentangle structural and physiological responses across Central and Western Africa. Three key indicators were used: evapotranspiration (ET), relative solar-induced chlorophyll fluorescence (SIF_rel), and the ratio of midday to midnight vegetation optical depth (VOD_ratio), which respectively, represent water flux, photosynthetic activity, and water regulation. A random forest model, combined with SHapley Additive exPlanations (SHAP) analysis, was used to separate vegetation anomaly signals and identify key climatic controls. The results reveal pronounced differences in vegetation responses between arid and humid climatic regions. In arid regions, near-infrared reflectance of vegetation (NIRv) and solar-induced chlorophyll fluorescence (SIF) exhibited clear negative anomalies and significant pre-drought declines, accompanied by marked changes in vegetation optical depth (VOD), indicating canopy structural damage and reduced photosynthetic activity. In contrast, trend analysis revealed that although SIF and NIRv in humid regions showed relatively strong responses during the pre-drought phase, they did not exhibit significant trends after the drought peak, and changes in VOD were comparatively small, suggesting that higher water availability partially buffered the prolonged impacts of drought on vegetation structure and function. Process analysis showed that three months before and after drought peaks, physiological indicators exhibited strong anomalies that closely tracked drought duration. SIF_rel, ET signals peaked earlier than water-content anomalies (VOD_ratio), suggesting a two-phase regulation strategy: early stomatal closure followed by delayed deep-root water uptake. Physiological anomalies accounted for over 88% of total vegetation anomalies during drought peaks, highlighting their dominant role in early-stage drought response. Precipitation and temperature emerged as primary drivers, explaining 76.8% of photosynthetic variation, 60.3% of ET variation, and 53.9% of water-content variation in the development. The recovery is influenced by the duration of drought and the regrowth of vegetation. By explicitly decoupling physiological and structural vegetation responses, this study provides refined, process-based insights into African ecosystem adaptation to water stress. These findings contribute to more accurate drought monitoring, water availability assessment, and climate adaptation strategies, directly supporting sustainable water and land management goals. Full article

Poly(3,4-ethylenedioxythiophene)–polystyrene sulfonate (PEDOT:PSS) is widely used to fabricate conductive organic coatings for electrodes in electrophysiology. As these devices move toward clinical translation, establishing sterilization methods that preserve their functional properties is essential. Ethylene oxide (EtO) is routinely used for sterilizing heat- and moisture-sensitive medical devices due to its high penetration efficiency and low thermal load. However, the absence of systematic studies evaluating its impact on PEDOT:PSS raises concerns about the compatibility of EtO sterilization with organic electrophysiology interfaces. Here, we report the first comprehensive evaluation of EtO sterilization on PEDOT:PSS electrodes electrochemically deposited onto cortical interfaces designed for intraoperative monitoring and stimulation. EtO exposure induced only minimal changes in surface topography, with no detectable alteration of the electrical or electrochemical performance of the electrodes. Impedance spectroscopy, cyclic voltammetry, and charge-injection capacity measurements all revealed that EtO-treated electrodes retained properties comparable to untreated controls. Moreover, EtO-sterilized PEDOT:PSS coatings demonstrated robust long-term stability under accelerated lifetime testing, exhibiting negligible degradation over extended operation. These findings demonstrate that EtO sterilization is fully compatible with PEDOT:PSS-based bioelectronic interfaces and constitutes a viable pathway toward their safe and effective integration into clinical electrophysiology. This work represents an important step toward translating organic conducting polymer technologies into real-world biomedical applications. Full article