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Retinal disease classification from spectral domain optical coherence tomography using lesion attentive fusion
Transcranial ultrasound stimulation of the subthalamic nucleus modulates cognitive function in humans
A multi-dataset crop yield prediction framework using adaptive cooperative metaheuristic optimization
Abstract Crop yield prediction is an essential element for agricultural planning and policy development, although the results may be less accurate because of the high variability that could exist in soil types, weather patterns, and crop growing dynamics for each region. The existing approaches for crop yield prediction have been hampered by the fact that they have been using limited data sources and rigid hyperparameter optimization techniques, thus inhibiting the ability to cope with the diversified agricultural data. The problem encountered by the existing approaches is addressed by the proposed work. The proposed framework focuses on the comprehensive integration of crop yield statistics, climatic variables, soil property data, and satellite-based vegetation indices acquired from various open-access datasets. This multi-source data integration thus allows capturing complementary information in the models regarding crop growth, which is not possible in any of the single datasets alone. To determine the optimized parameter settings for the models, a novel adaptive cooperative PSO–DE–Jaya (AC-PDJ) metaheuristic is proposed by hybridizing the global searching capability of PSO, the variation mechanism of DE, and the exploitation strength of the Jaya algorithm. The cooperation among the participating strategies will be adaptively regulated to enhance the convergence with better preservation of solution diversity. The proposed methodology will be evaluated using various machine learning and deep learning models and compared against various state-of-the-art optimization techniques. Experimental results on different crops in various geographical locations have demonstrated that AC-PDJ-optimized models result in lower prediction errors and improved goodness-of-fit with faster and more stable convergence performance. The framework uses only opensource datasets and computation analysis, hence making it suitable for practical expert decision-support applications with no requirement of actual field experiments or specialized equipment.
Detecting river-stage regime shifts in Polish rivers using validated multi-mission satellite altimetry observations
Instruments of Hybrid Warfare in Iran-Russian Cooperation: Implications for Georgia
The modern global security architecture in the world is undergoing a fundamental transformation. The order established after the Cold War is being actively confronted by the growing alliance between Iran and the Russian Federation. With its crucial geostrategic and transit location, the South Caucasus has become one of the main epicenters of this global confrontation. Against the backdrop of Russia's military aggression in Ukraine, which disrupted the traditional balance of power, Moscow is forced to deepen its strategic coordination with Tehran to maintain its regional positions. This article deeply explores the strategy of the Russia-Iran partnership and the specifics of the hybrid warfare instruments they employ in Georgia and the South Caucasus as a whole. The research relies on the latest approaches to regional security and analyzes asymmetric tactics, such as: cyber sabotage, psychological and information operations, ideological infiltration (soft power), and economic coercion used by the authoritarian tandem of Russia and Iran. Based on this research, it was revealed that the threats directed against Georgia's democratic institutions and Euro-Atlantic aspirations no longer solely encompass the Kremlin's hard power and occupation. Added to all this is Iran's progressively growing religious, economic, and intelligence penetration, which is in full alignment with the Kremlin's aspirations to completely neutralize Western influence in the region. Additionally, the presented report discusses the threats associated with geopolitical projects such as the "3+3" format and the risks of Georgia's potential use in sanctions evasion schemes.
Sustainable sludge derived activated carbon polyaniline composite removes lead and cadmium from industrial wastewater
The Role of Children’s Literature in Teaching the Holocaust and Intergenerational Trauma
This paper examines the role of children’s literature in teaching about the Holocaust and intergenerational trauma. We explore the role of children’s literature in transmitting and preserving the memories of the Holocaust. We also discuss Adorno’s essay titled “Never Again: Education after Auschwitz” and the ways he viewed early childhood education as the ideal stage for the type of education essential to address antisemitism. We then discuss the resources provided by the United States Holocaust Memorial Museum for selecting appropriate Holocaust literature. We also explore the educational challenge of teaching about the Holocaust to preserve its memory. We conclude by showing how historical fiction is one of the many resources for teaching about difficult or sensitive topics to guard against genocide, trauma, and the Holocaust.
Interpretable integration of CITE-seq RNA and ADT profiles without explicit modality-weight tuning via tensor decomposition-based unsupervised feature extraction
Evaluation of perimenstrual atopy in apparently healthy females through hormonal, immune, and autonomic changes across menstrual phases: a prospective study
Drug-based ionic derivatives of NSAIDs: influence of cation structure on physicochemical properties, skin permeation, and biocompatibility
Abstract Drug-based ionic systems represent a promising strategy to improve the physicochemical properties and delivery performance of poorly soluble active pharmaceutical ingredients. In this study, ionic derivatives of four nonsteroidal anti-inflammatory drugs (NSAIDs)—ibuprofen, ketoprofen, naproxen, and salicylic acid—were synthesized using three organic cations: didecyldimethylammonium (DDA), 1-ethyl-3-methylimidazolium (EMIM), and cholinium (Chol). The obtained systems were compared with the parent drugs and their sodium salts in terms of thermal stability, viscosity, solubility, lipophilicity, transdermal permeation, and cytotoxicity. Conversion of NSAIDs into ionic forms significantly improved aqueous solubility, increasing from 0.039 to 0.142 g·dm -3 for the parent drugs to 20.5–178.3 g·dm -3 for EMIM- and cholinium-based systems, while sodium salts exhibited extremely high solubility (151–232 g·dm -3 ). Rheological analysis revealed strong cation-dependent viscosity differences, ranging from approximately 0.2–8.0 Pa·s for EMIM systems to ~ 175 Pa·s for [Chol][KETO] at 20 °C. In vitro permeation studies demonstrated that ionic modification substantially affected transdermal transport. For ibuprofen, cumulative permeation after 24 h increased from 379 µg·cm⁻ 2 (parent drug) to 1629 µg·cm -2 for [EMIM][IBU] and 1709 µg·cm -2 for [Chol][IBU]. Naproxen derivatives also showed significant enhancement, reaching ~ 797 µg·cm -2 for [Na][NAP] and ~ 759 µg·cm -2 for [EMIM][NAP] compared with 235 µg·cm -2 for the parent compound. In contrast, DDA-based systems generally reduced permeation. Ketoprofen derivatives exhibited moderate enhancement (up to ~ 1.5-fold), while salicylic acid showed formulation-dependent behaviour. Overall, the results demonstrate that the structure of the organic cation significantly influences the physicochemical properties, skin permeation, and biocompatibility of NSAID-derived ionic systems.
Climate risk and corporate green innovation bubbles: evidence from Chinese listed enterprises
Abstract Against the backdrop of global climate change and China’s “dual carbon” goals, climate risk has become a critical external factor shaping corporate sustainable development strategies in emerging economies. Using a sample of China’s A-share listed companies from 2010 to 2023 and a two-way fixed-effects panel model, this paper empirically examines the impact of climate risk on corporate green innovation bubbles—a phenomenon characterized by a systematic divergence between green patent quantity and quality. Climate risk is found to significantly exacerbate green innovation bubbles, as firms facing greater climate uncertainty systematically prioritize patent quantity over substantive technological quality. Managerial myopia and financing constraints are two key transmission mechanisms through which climate risk distorts the allocation of green innovation resources. Digital transformation and ESG performance unexpectedly amplify rather than mitigate these distortionary effects. Heterogeneity analysis further reveals that the bubble-inducing effect of climate risk is more pronounced among non-state-owned enterprises, low-carbon-emission firms, and firms with higher managerial ownership. These findings extend research on the economic consequences of climate uncertainty and carry important implications for the design of green innovation evaluation systems, climate disclosure policy, and sustainable finance governance in emerging markets.
Research on the evolutionary trajectories of intelligent energy technology based on main path analysis and SAO semantic analysis
Efficient removal of regorafenib from aqueous solutions using KOH-activated hazelnut husk-derived activated carbon: adsorption mechanism, kinetics, isotherms and thermodynamics
Hierarchical reinforcement learning with low-level agents and active curriculum for torpedo countermeasures
Prevalence of colour vision deficiency among Schoolchildren at Repi Primary school in Addis Ababa, Ethiopia
Harmonic resonance analysis in PMSG based wind farms: comparison of simplified and detailed impedance models and passive and active damping
Design rule formalization from documented requirements for detailed 3D CAD model verification
Source limitation drives slow compositional recovery in tropical secondary forests
Tropical forests can recover diversity, structure, and function rapidly after disturbance. However, plant compositional recovery remains incomplete even after many decades, and the reasons for this lag are poorly understood. We investigated the roles of source, dispersal, and establishment limitation in affecting compositional recovery in Costa Rican forests using long-term data on species composition of trees, seeds, seedlings, and saplings, along with measurements of herbivory and leaf pathogen damage in seedlings and saplings from old-growth (OG) and second-growth (SG) forest plots. We classified tree species into successional groups, including: generalist (similar relative abundance in OG and SG), old-growth specialist (higher relative abundance in OG), old-growth exclusive (detected only in OG), and too rare to classify conclusively. Infrequent species drove community dissimilarity between old-growth and second-growth plots, and 40% of species in old-growth plots were absent as trees in second-growth forests up to 55 y old. Old-growth exclusive species (as trees and seedlings and saplings in OG plots and as seeds in seed traps in OG and SG) have extremely low abundance (11 trees/ha; 1 seedling or sapling/ha; 8 seeds/ha), indicating strong source limitation. Old-growth exclusive species shared seed dispersal traits with old-growth specialists established in SG, providing no evidence of dispersal limitation. Old-growth specialist seedlings and saplings had higher survival in SG than in OG, providing no evidence of establishment limitation. These findings suggest source limitation as the primary driver of delayed compositional recovery in mid-to-late stages of forest succession and underscore the potential for targeted enrichment plantings to accelerate full compositional recovery.
No gender differences in predictive processing
Abstract Statistical learning, defined as the implicit extraction of environmental regularities, is considered a fundamental cognitive mechanism that supports predictive processing across individuals, domains, and species. However, whether it is modulated by gender remains unclear. This study investigated potential gender-related differences in statistical learning using an age-matched sample of 129 women and 129 men ( N = 258) who completed a well-established visuomotor probabilistic learning task. Statistical learning was revealed in both reaction time and accuracy measures, with participants responding faster and more accurately to high-probability than to low-probability trials. Critically, neither the magnitude nor the trajectory of statistical learning differed between women and men. Furthermore, no baseline differences in visuomotor performance interacted with learning metrics. Overall, these findings suggest that implicit statistical learning is a robust cognitive mechanism that is highly resilient to gender-related variation.