Search Results (39,761)

This study examines the relationship between domestic savings and fixed capital formation in African economies, focusing on the moderating role of institutional quality. Using panel data for 45 African countries over the period 1996–2024, we estimate a fixed-effects model with Driscoll–Kraay standard errors to account for cross-sectional dependence and heteroskedasticity. The findings confirm a positive and significant relationship between domestic savings and investment, consistent with traditional growth theory. More importantly, the results reveal that institutional quality significantly enhances the effectiveness of savings in promoting fixed capital formation, with stronger governance frameworks improving the allocation of resources and limiting diversion into unproductive uses. Disaggregated results further show that the moderating effect varies across institutional dimensions and is more pronounced in middle-income countries, while remaining weak or insignificant in low-income economies. These findings highlight the conditional nature of the savings–investment nexus and underscore the importance of institutional development in translating domestic resources into productive investment in Africa. Full article

High-resolution marine sedimentary archives from active continental margins provide exceptional records of paleo-earthquake histories through the preservation of seismogenic turbidites. A 477cm long gravity core (HOBAB4-S2) retrieved from the southern Okinawa Trough (SOT) off eastern Taiwan at a water depth of 1505 m preserves a ~700-year record (1445–2003 AD) of alternating hemipelagic background sedimentation and rapid event deposits. Chronological control is established through accelerator mass spectrometry (AMS ¹⁴C) radiocarbon dating and excess ²¹⁰Pb profiling. Detailed sedimentological and geochemical analyses identify fifteen turbidite events (T1–T15), which are grouped into three lithofacies: TI (silt turbidites), TII (sandy silt turbidites), and TIII (silty sand turbidites). Temporal correlation demonstrates that the majority of these turbidite beds coincide with documented historical and instrumental earthquakes (M_w ≥ 6.6), confirming seismic shaking as the primary triggering mechanism. Notably, turbidite thickness, basal grain size and recurrence frequency increase during the Little Ice Age (LIA, ~1400–1850 AD), a pattern we attribute to enhanced terrestrial sediment supply driven by intensified typhoon activity, which preconditioned the slope system for seismically induced failure. These findings demonstrate that turbidite sequences in the SOT provide a reliable record of local paleo-earthquake activity, while establishing that their stratigraphic expression is modulated by climatically driven sedimentary preconditioning. This principle is critical to paleoseismic reconstruction in seismically active, storm-prone continental margins globally. Full article

The North China Craton hosts abundant skarn iron resources, yet the regional large-scale mineralization mechanism remains incompletely understood. The Litun deposit is a newly discovered skarn iron deposit in the North China Craton. Integrated field geological investigations, petrographic observations, fluid inclusion microthermometry and stable isotope geochemistry are applied to constrain evaporite contributions to metallogenic processes. Four mineralization stages are identified: skarn, oxide, sulfide, and carbonate. Early skarn-stage fluids are iron-rich magmatic hydrothermal fluids with high temperatures (498 to >550 °C), high salinities (18.6 to 59.4 wt% NaCl eqv.), and magmatic δ¹⁸O values of 8.3 to 10.8‰. Subsequent oxide to late carbonate stages record continuous infiltration of meteoric water, supported by H–O isotopic trends of rising meteoric water proportions. Pyrite from the magnetite ores has δ³⁴S_V-CDT values between 12.0 and 15.0‰, significantly higher than those of pyrite in the Litun diorite (−0.8 to 1.1‰), indicating the contributions of sulfur from evaporites (δ³⁴S_V-CDT 26.9 to 28.6‰) in the mineralization process. Moreover, vein pyrite formed in later stages displays even higher δ³⁴S values (17.3 to 20.9‰), demonstrating progressive enrichment of evaporite-derived sulfur as hydrothermal activity evolves. Synchronous rises in meteoric water fraction and evaporite sulfur proportion indicate evaporites are delivered into the ore-forming system via meteoric water mixing. The mixing of meteoric water containing dissolved evaporites and iron-rich magmatic-hydrothermal fluids may be the major mechanism of magnetite precipitation in the Litun deposit. Full article

Endophytic fungi from Malaysian medicinal plants constitute a metabolically prolific yet underexplored reservoir for natural product discovery. This scoping review of 56 studies published between 2015 and 2025 identified a fundamental methodological divergence within the field: while phenotypic bioactivity screening dominates the literature (>87% of studies), it is weakly supported by chemical characterization (<25%) and entirely disconnected from genomic investigation (0% biosynthetic gene cluster studies). This phenotype-first paradigm has largely confined the field to descriptive reporting, limiting mechanistic understanding and translational potential. Collectively, the evidence reveals a substantial disconnect between reported bioactivities and their underlying biosynthetic foundations. To address this limitation, a practical genotype-to-phenotype workflow is proposed that integrates strain prioritisation, multi-omics-guided activation, chemical mapping, and mechanism-oriented validation. By linking genomic potential with metabolite production and biological function, this framework provides a roadmap for advancing fungal natural product discovery beyond conventional phenotype-driven screening. Adoption of such approaches may improve the identification of chemically novel and biologically relevant metabolites while supporting the sustainable development of Malaysia’s endophytic fungal resources for biotechnological and pharmaceutical applications. Full article

At 1 km resolution, NDVI projections for heterogeneous landscapes can appear spatially coherent in aggregate while concealing substantial class-level prediction weaknesses, a limitation that has received limited systematic attention in the NDVI projection literature. This study applies a four-component assessment workflow to Northeast China (NEC) for 2040 under SSP1-2.6, SSP2-4.5, and SSP5-8.5, integrating multi-stage model selection, land-cover-stratified validation, quantile-regression-based uncertainty characterization, and validation-priority ranking. Among three candidate tree-based models evaluated using spatial block cross-validation, temporal holdout validation, long-jump extrapolation, and climatic perturbation tests, LightGBM showed the most balanced and consistent performance, with spatial CV R² = 0.654 ± 0.123, temporal holdout R² = 0.710, and long-jump R² = 0.671, and was therefore selected for the 2040 projection. Projected regional mean NDVI increased modestly from 0.393 in 2020 to 0.414–0.417 across scenarios, with limited divergence among SSP pathways at this near-term horizon. Class-stratified validation of the 2020 holdout prediction revealed that global model performance masked strong class-level heterogeneity, with R² values ranging from 0.576 for Construction land to −0.886 for Unused land. Water bodies and Unused land exhibited negative R² values, indicating weak class-level predictive support relative to a simple class-mean benchmark. Residual decomposition showed that Water bodies combined high random error with elevated systematic deviation, whereas Unused land was mainly characterized by systematic bias, suggesting different needs for class-specific model improvement. The Uncertainty Risk Index (URI), derived from 95% prediction intervals, was highest in Construction land and lowest in Cropland across all scenarios. Integrating historical residuals with future URI-identified Water bodies, Unused land, and Construction land as the highest-priority classes for future targeted validation. These priorities arise from both limited class representation and intrinsic NDVI-related complexity, including low vegetation signal, mixed-pixel effects, and heterogeneous land-surface composition. These results demonstrate that land-cover-stratified error decomposition and uncertainty-informed priority ranking reveal class-specific projection limitations that aggregate accuracy metrics can conceal. Full article

Global warming is associated with an increased frequency and intensity of heat waves, which severely threaten crop production and sustainable agriculture. As sessile organisms, plants evolved complex heat stress memory mechanisms to cope with recurring heat waves. Heat shock transcription factors (HSFs) are at the core of plant heat stress responses and memory. They regulate basal thermotolerance, acquired thermotolerance, and the maintenance of acquired thermotolerance. These processes involve multiple mechanisms, including temperature perception, activation of heat shock protein expression, and integration of hormonal and epigenetic signals. Here, we review the pivotal role HSFs play in the formation of heat stress memory, their structural characteristics, functional differentiation, and signal perception and transcriptional regulatory mechanisms. We further discuss the functional conservation and the diversity of HSFs across multiple species—for instance, HSFA2 acts as a conserved regulator of heat stress memory in Arabidopsis, tomato, wheat, and barley—and outline future research directions, including the functional characterization of heat shock transcription factor (HSF) subfamilies, investigation of their roles under stress combination, and strategies to balance stress tolerance with growth and development. We hope that our review will provide a theoretical foundation for the genetic improvement of crop thermotolerance as well as contribute to efforts directed at ensuring food security in the face of climate change. Full article

In this study, five Fe-rich scoria-based glass compositions were prepared using natural scoria, recycled glass cullet, limestone, and magnesite through the melt-quenching technique at a temperature of 1400 °C for 2 h. The effect of Fe₂O₃ content (2.9–14.5 wt%) on the thermal behavior, crystallization, density, and predicted optical properties of glass was investigated. Differential thermal analysis revealed that increasing Fe₂O₃ content leads to a variation in glass transition (Tg = 632–669 °C) and an increase in softening temperatures (Ts = 711–737 °C), accompanied by an expanded thermal stability window (∆T = Tx − Tg) up to 254 °C, indicating enhanced resistance to crystallization and improved thermal stability. The density measurement showed a non-monotonic variation with composition, due to the combined effect of Fe₂O₃ enrichment and network structural modification. The crystallization behavior of the Fe-rich scoria-based glass (H50) was further studied after heat treatment at 900 °C and at 950 °C using XRD and SEM analysis. The heated samples exhibited the formation of crystalline phases including diopside, gehlenite, wollastonite, maghemite, and anorthite. While SEM observation revealed progressive crystal growth and microstructural densification with increasing heat treatment temperature, indicating the transformation from glass to glass–ceramic. In addition, a semi-empirical optical assessment based on literature-derived models suggested increased absorptance from 97.26% to 98.83% and reduced reflectance with increasing Fe₂O₃ content. However, these optical parameters show theoretical estimates and require experimental validation. These findings demonstrate the potential of Fe-rich scoria-based glasses as thermally stable materials for high-temperature and energy-related applications while using natural and industrial waste sources. Full article

The coast redwood forest, populated by the ancient relict species Sequoia sempervirens, provides unique and essential ecological services along the Pacific coast of California. It is a haven for endemism and ecological diversity, offers habitat for threatened species, and is an important global terrestrial carbon sink. However, a long history of resource extraction has significantly impacted this ecosystem. Complex old-growth forests have largely been replaced with managed timber stands, and biological diversity has been reduced through the loss of habitat and basic ecological functions. Under natural conditions, coast redwood is highly resilient to disturbance, due to its propensity for basal and epicormic sprouting. The primarily clonal reproductive strategy of S. sempervirens allows for natural thinning as a stand matures, generally leading to the development of late-seral characteristics without the need for active restoration. The increasingly pervasive use of active silvicultural tools for restoration, such as forest thinning and commercial timber harvest, can create a density-driven cycle that requires periodic re-application of the treatment and hinders natural successional processes. In order to restore forest health and resiliency, natural successional processes inherent to coast redwood can be supported as a restoration alternative. Full article

Whole-plant soybean (WPS) is a high-protein forage resource, but its natural ensiling is often unsatisfactory due to low water-soluble carbohydrate content and high buffering capacity. This study investigated the effects of exogenous melatonin (ME) at 0 (CK), 5 (ME1), 10 (ME2), and 20 (ME3) mg/kg fresh matter on fermentation quality, chemical composition, in vitro rumen fermentation, and bacterial community structure of WPS silage. ME2 and ME3 had lower pH values and higher lactic acid contents than CK, with both treatments achieving pH values below 4.2. Crude protein concentration increased from 15.42% in CK to 19.96% in ME3, while neutral detergent fiber was lower in all ME treatments, and acid detergent fiber was lower in ME2 and ME3 than in CK. At 36 h, no overall treatment effect was detected for cumulative gas production, whereas in vitro dry matter digestibility differed only between ME2 and ME3. 16S rRNA gene sequencing revealed that ME altered the bacterial community, with community-weighted rrn copy number elevated in ME2 and ME3. Random forest analysis identified Enterococcus as the genus with the highest importance for treatment classification, and functional predictions indicated higher predicted abundances of amino acid biosynthesis pathways in ME treatment groups. These results indicate that ME has potential as an additive for improving WPS silage fermentation, but practical dosage recommendations require further validation through aerobic stability, animal performance, economic, and safety assessments. Full article

Treatment of non-small cell lung cancer (NSCLC) has been transformed by immunotherapy and targeted therapies. We aimed to characterize clinical features, treatment patterns, and healthcare resource use in patients with early and locally advanced NSCLC before incorporation of these therapies. This retrospective observational study included adults diagnosed with stage I–III NSCLC at four Spanish hospitals between 2014 and 2018, with follow-up until 2021, using artificial intelligence to extract data from electronic health records. A total of 951 patients were included (34.7% stage I, 16.7% stage II, 48.6% stage III), with a median age of 66 years and 31.9% female. Surgery was performed in 78.5% of stage I, 74.8% of stage II, and 35.5% of stage III patients. Among surgical patients, 62.5% received adjuvant chemo- and/or radiotherapy, 20.8% neoadjuvant therapy, and 15.7% both; among non-surgical patients, chemoradiotherapy was the most common treatment (50.4%). Beyond hospitalization, outpatient visits were the most frequently used healthcare resource. These findings provide a historical benchmark of NSCLC care before introduction of immunotherapy and targeted therapies in these settings, highlighting treatment variability and the need for earlier diagnosis, structured treatment pathways, and multidisciplinary management. Full article

Cannabis sativa L. is an economically significant multi-use crop valued for fiber, seed, and phytochemical production. Compared with other crops, advancement in Cannabis sativa has been slow due to regulatory constraints and genetic resource limitations. Recent advances in technology have transformed the research landscape, supporting a deeper understanding of the genetic architecture underlying key agronomic traits. This review summarizes current progress in Cannabis sativa genetics and genomics, mainly focusing on structural genome organization, including chromosome-level assemblies and emerging pangenomic resources that capture species-wide diversity. We explore the molecular basis of key agronomic traits, including sex determination, cannabinoid biosynthesis, fiber quality, seed composition, disease resistance, and abiotic stress tolerance, highlighting their complex regulatory networks. Functional genomics tools including virus-induced gene silencing, transient expression systems, and CRISPR/Cas9 genome editing are reviewed as approaches enabling direct gene functional validation. We further review integration of these resources with molecular breeding strategies, including marker-assisted and genomic selection, to accelerate elite genotype development. Finally, we address persistent challenges such as genomic complexity, reference bias, and phenotyping limitations while outlining future research directions. Together, these advances position C. sativa as a compelling system for both fundamental plant biology and applied crop improvement. Full article

Microplastic pollution in tropical urban rivers has become an increasing environmental concern due to rapid urbanization, inadequate waste management, and hydrological transport processes. This study investigated the occurrence, characteristics, and spatiotemporal distribution of microplastics in the Can Tho River, Vietnam, along an urban–peri-urban–rural gradient during dry and wet seasons. Surface-water samples were collected at 15 sites and analyzed for microplastic abundance, density, shape, color, and size composition using stereomicroscopic identification and statistical analyses. Microplastics were detected at all sampling sites in both seasons, indicating widespread contamination throughout the river system. Although seasonal differences in overall abundance and density were not statistically significant at the basin scale, clear spatial variability was observed, particularly in urban and peri-urban regions. Fibers and fragments were the dominant shapes, while blue, purple, and green particles were the most common color categories. Particles larger than 1000 µm accounted for the largest proportion of detected microplastics, and continuous size-distribution analysis revealed broadly similar overall distributions, although a greater proportion of smaller particles was observed during the dry season. The results suggest that hydrological conditions, urbanization, and land-use characteristics may contribute to the observed spatial and seasonal patterns of microplastic distribution in the Can Tho River. Peri-urban zones exhibited the greatest seasonal variability, highlighting their role as transitional areas that may influence microplastic redistribution in tropical river systems. This study provides baseline information for understanding microplastic pollution in the Mekong Delta and supports future river management strategies. Full article

The sustainable valorization of spontaneous flora biomass for the recovery of high value-added phytochemicals represents a key opportunity within the circular bioeconomy, yet it remains constrained by the environmental limitations of conventional extraction solvents and the lack of data-driven optimization frameworks. In this study, Natural Deep Eutectic Solvents (NADES) composed of betaine and 1,3-propanediol were designed and applied as bio-based extraction media for the recovery of bioactive metabolites from Artemisia annua L. spontaneous biomass in the context of green extraction and sustainable resource utilization. Two liquid–solid extraction techniques, namely vortex-assisted extraction and ultrasound-assisted extraction, were evaluated. The influence of key process parameters, including the eutectic component molar ratio, water content, solid-to-liquid (S/L) ratio, extraction temperature, and extraction time, was systematically investigated. Results demonstrated that extraction efficiency was strongly dependent on both solvent composition and process conditions, with distinct optimum parameters for different phytochemical classes. Maximum total polyphenol content (52.08 mg GAE/mL) was achieved via ultrasound-assisted extraction at 20 °C for 15 min, using a 1:3 NADES ratio with 40% water dilution and S/L = 1:5, while the highest flavonoid yield (17.34 mg QE/mL) was obtained by vortex-assisted extraction for 45 min using a 1:6 NADES ratio under the same dilution and S/L conditions. To identify extraction conditions associated with improved process efficiency, a hybrid modeling approach combining deep neural networks with the Success-History-based Adaptive Differential Evolution (SHADE) algorithm was employed, enabling high-accuracy prediction of extraction performance across a broad parameter space. The proposed framework demonstrates the feasibility of integrating green solvent design with machine learning-driven process modeling for the efficient valorization of underutilized plant biomass, contributing to the development of resource-efficient, sustainable extraction protocols, consistent with principles of process intensification and resource-efficient extraction strategies. Full article

Urban Green Spaces (UGSs) monitoring usually focuses on the extraction of vegetation in the physical layer, while neglecting their functional attributes. This renders the monitoring results unable to objectively reflect the rationality of UGS planning. To address these issues, this study proposes a multi-level classification method integrating multi-source remote sensing and geospatial big data to bridge the semantic gap between the physical layer and the functional layer. In this method, a strategy of prior knowledge injection and semantic reconstruction was developed through the fine-tuning of a BERT model with cross-mapping rules. This strategy aims to classify the urban area into 24 functional categories, generating the social-functional basemap in a functional layer, based on Point of Interest (POI), OpenStreetMap (OSM), and Global Urban Boundary (GUB). Meanwhile, a novel deep learning architecture, namely the Multi-Shape and Spectral Aware Network (MSSANet), was designed for precise vegetation classification of UGSs in the physical layer. Finally, a “function-first, vegetation-second” coupling paradigm containing three functional attribute layers, referring to the Code for Classification of UGS in China (CJJ/T 85-2017), was established. This paradigm integrates the social-functional basemap with physical vegetation patches to build a multi-level UGS classification framework, i.e., the 5 major UGS categories, 11 intermediate UGS categories, and 24 fine-grained UGS sub-categories. Experiments conducted in Jinan and Qingdao, China, demonstrate the efficacy of the proposed method for refined multi-level UGS mapping. Full article