Search Results (768)

In order to study how indigenous microorganisms can enhance the strength properties of granite residual soil in the Hanzhong area, two Bacillus species that produce urease were isolated from the local soil. The two Bacillus species are Bacillus subtilis and Bacillus tequilensis, and they were used for the solidification and improvement of the granite residual soil. Unconfined compressive strength tests, scanning electron microscope (SEM) and X-ray diffraction (XRD) analyses were systematically used to analyze the influence and mechanism of different cementation solution concentrations on the improvement effect. It has been found that with the growth of cementing fluid concentration, the unconfined compressive strength of improved soil specimens shows an increasing tendency, reaching its highest value when the cementing solution concentration is 2.0 mol/L. Among different bacterial species, curing results vary; Bacillus tequilensis demonstrates better performance across various cementing solution concentrations. The examination of failure strain in improved soil samples indicates that brittleness has been successfully alleviated, with optimal outcomes obtained at a cementing solution concentration of 1.0 mol/L. SEM and XRD analyses show that calcium carbonate precipitates (CaCO₃) are formed in soil samples treated by both strains. These precipitates effectively bond soil particles, verifying improvement effects on a microscopic level. The present study proposes an environmentally friendly and economical method for enhancing engineering applications of granite residual soil in Hanzhong area, which holds significant importance for projects such as artificial slope filling, subgrade filling, and foundation pit backfilling. Full article

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article

Groundwater is a critical yet understudied resource in Peru, where surface water has traditionally dominated national assessments. This study provides the first country-scale analysis of groundwater storage (GWS) variability in Peru from 2003 to 2023 using satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions. We used the GRACE Data Assimilation-Data Mass Modeling (GRACE-DA-DM GLV3.0) dataset at 0.25° resolution to estimate annual GWS trends and evaluated the influence of El Niño–Southern Oscillation (ENSO) events and anthropogenic extraction, supported by in situ well data from six major aquifers. Results show a sustained GWS decline of 30–40% in coastal and Andean regions, especially in Lima, Ica, Arequipa, and Tacna, while the Amazon basin remained stable. Strong correlation (r = 0.95) between GRACE data and well records validate the findings. Annual precipitation analysis from 2003 to 2023, disaggregated by climatic zone, revealed nearly stable trends. Coastal El Niño events (2017 and 2023) triggered episodic recharge in the northern and central coastal regions, yet these were insufficient to reverse the sustained groundwater depletion. This research provides significant contributions to understanding the spatiotemporal dynamics of groundwater in Peru through the use of satellite gravimetry data with unprecedented spatial resolution. The findings reveal a sustained decline in GWS across key regions and underscore the urgent need to implement integrated water management strategies—such as artificial recharge, optimized irrigation, and satellite-based early warning systems—aimed at preserving the sustainability of the country’s groundwater resources. Full article

This study investigates the fabrication and residual stress behavior of a 0.5 wt.% graphene-reinforced AA2195 aluminum matrix composite, developed for advanced aerospace structural applications. The composite was synthesized via squeeze casting, followed by a multi-pass hot rolling process and subsequent T8 heat treatment. The evolution of residual stress was systematically examined after each rolling pass and during thermal treatments. The successful incorporation of graphene into the matrix was confirmed through Energy-Dispersive Spectroscopy (EDS) analysis. Residual stress measurements after each pass revealed a progressive increase in compressive stress, reaching a maximum of −68 MPa after the fourth hot rolling pass. Prior to the fifth pass, a solution treatment at 530 °C was performed to dissolve coarse precipitates and relieve internal stresses. Cold rolling during the fifth pass reduced the compressive residual stress to −40 MPa, and subsequent artificial aging at 180 °C for 48 h further decreased it to −23 MPa due to recovery and stress relaxation mechanisms. Compared to the unreinforced AA2195 alloy in the T8 condition, which exhibited a tensile residual stress of +29 MPa, the graphene-reinforced composite in the same condition retained a compressive residual stress of −23 MPa. This represents a net improvement of 52 MPa, highlighting the composite’s superior capability to retain compressive residual stress. The presence of graphene significantly influenced the stress distribution by introducing thermal expansion mismatch and acting as a barrier to dislocation motion. Overall, the composite demonstrated enhanced residual stress characteristics, making it a promising candidate for lightweight, fatigue-resistant aerospace components. Full article

Hippophae rhamnoides subsp. sinensis is extensively found in China, where the annual precipitation ranges from 400 to 800 mm. It is the most dominant species in natural sea buckthorn forests and the primary cultivar for artificial ecological plantations. Additionally, it exhibits significant nutritional and medicinal value, making it a renowned eco-economic tree species. Despite extensive research into its ecological functions and health benefits, the mitochondrial genome of this widespread species has not yet been published, and knowledge of the mitochondrial genome is crucial for understanding plant environmental adaptation, evolution, and maternal inheritance. Therefore, the complete mitochondrial genome was successfully assembled by aligning third-generation sequencing data to the reference genome sequence using the Illumina NovaSeq 6000 platform and Nanopore Prometh ION technologies. Additionally, the gene structure, composition, repeat sequences, codon usage bias, homologous fragments, and phylogeny-related indicators were also analyzed. The results showed that the length of the mitochondrial genome is 454,489 bp, containing 30 tRNA genes, three rRNA genes, 40 PCGs, and two pseudogenes. A total of 411 C-to-U RNA editing sites were identified in 33 protein-coding genes (PCGs), with higher frequencies observed in ccmFn, ccmB, nad5, ccmC, nad2, and nad7 genes. Moreover, 31 chloroplast-derived fragments were detected, accounting for 11.86% of the mitochondrial genome length. The ccmB, nad4L, and nad7 genes related to energy metabolism exhibited positive selection pressure. The mitochondrial genome sequence similarity between H. rhamnoides subsp. sinensis and H. tibetana or H. salicifolia was 99.34% and 99.40%, respectively. Fifteen shared gene clusters were identified between H. rhamnoides subsp. sinensis and H. tibetana. Phylogenetically, the Rosales order showed close relationships with Fagales, Fabales, Malpighiales, and Celastrales. These findings provide fundamental data for exploring the widespread distribution of H. rhamnoides subsp. sinensis and offer theoretical support for understanding the evolutionary mechanisms within the Hippophae genus and the selection of molecular breeding targets. Full article

The use of artificial intelligence tools in agriculture is growing. In particular, the use of ensemble models. However, there are still few reviews on the use of these models in the study of the impacts of climate change on agriculture. Therefore, the aim of this article is to review the use of such models and perform three key tasks: (1) identify topics in which ensemble models are used, (2) determine the most widely applied model and the predominant crops and regions, and (3) explore future applications and challenges. As a result, it was noted that the first studies, dating back to 2011, applied ensemble models to model invasive species. Since then, research has focused on changes in temperature and precipitation, with at least one study published every year. The most cited studies have dealt with land use classification, emphasizing its relevance to climate change studies. Notably, studies on carbon storage in soil and its capacity to remove CO₂ from the atmosphere have become increasingly relevant. This analysis highlights the growing importance of ensemble models in climate-related agricultural research, outlining trends and key areas for future exploration. Full article

The ecological environment of Inner Mongolia constitutes a critical component of China’s ecological civilization construction. To comprehensively assess and monitor ecological quality dynamics in this region, this study employed MODIS remote sensing data products (2000–2020) and derived four key indicators, —vegetation index (NDVI), wetness index (WET), build-up and soil index (NDBSI), and land surface temperature (LST)—via the Google Earth Engine (GEE) platform. A Remote Sensing-based Ecological Index (RSEI) was constructed using principal component analysis (PCA) to establish an annual long-term time series, thereby eliminating subjective bias from artificial weight assignment. Integrated methodologies—including Theil–Sen Median and Mann–Kendall trend analysis, Hurst exponent, and geographical detector—were applied to investigate the spatiotemporal evolution of ecological quality in Inner Mongolia and its responses to climatic and anthropogenic drivers. This study proposes a novel framework for large-scale ecological quality assessment using remote sensing. Key findings include the following: The mean RSEI value of 0.41 (2000–2020) indicates an overall improving trend in ecological quality. Areas with ecological improvement and degradation accounted for 76.06% and 23.84% of the region, respectively, exhibiting a spatial pattern of “northwestern improvement versus southeastern degradation.” Pronounced regional disparities were observed: optimal ecological conditions prevailed in the Greater Khingan Range (northeast), while the Alxa League (southwest) exhibited the poorest conditions. Northwestern improvement was primarily driven by increased precipitation, rising temperatures, and conservation policies, whereas southeastern degradation correlated with rapid urbanization and intensified socioeconomic activities. Our results demonstrate that MODIS-derived RSEI effectively enables large-scale ecological monitoring, providing a scientific basis for regional green development strategies. Full article

The progressive proliferation of sensor deployment in distribution networks (DNs), propelled by the dual drivers of power automation and ubiquitous IoT infrastructure development, has precipitated exponential growth in real-time data generated by multisource heterogeneous (MSH) sensors within multilayer grid architectures. This phenomenon presents dual implications: large-scale datasets offer an enhanced foundation for reliability assessment and dispatch planning in DNs; the dramatic escalation in data volume imposes demands on the computational precision and response speed of traditional evaluation approaches. The identification of critical influencing factors under extreme operating conditions, coupled with dynamic assessment and prediction of DN reliability through MSH data approaches, has emerged as a pressing challenge to address. Through a brief analysis of existing technologies and algorithms, this article reviews the technological development of MSH data analysis in DNs. By integrating the stability advantages of conventional approaches in practice with the computational adaptability of artificial intelligence, this article focuses on discussing key approaches for MSH data processing and assessment. Based on the characteristics of DN data, e.g., diverse sources, heterogeneous structures, and complex correlations, this article proposes several practical future directions. It is expected to provide insights for practitioners in power systems and sensor data processing that offer technical inspirations for intelligent, reliable, and stable next-generation DN construction. Full article

Harmful Algal Blooms (HABs) are a growing environmental concern due to their adverse impacts on aquatic ecosystems, human health, and economic activities. These blooms are driven by a combination of factors, including nutrient enrichment, environmental factors, and hydrological conditions, leading to the excessive growth of algae. HABs produce toxins that threaten aquatic biodiversity, contaminate drinking water, and cause economic losses in fisheries and tourism. The causes of HABs are multifaceted, involving interactions between environmental factors such as temperature, light availability, and nutrient levels. Agricultural runoff, wastewater discharge, and industrial pollution introduce excessive nitrogen and phosphorus into water bodies, fueling bloom formation. Climate change further exacerbates the problem by altering precipitation patterns, increasing water temperatures, and intensifying coastal upwelling events, all of which create favorable conditions for HAB proliferation. This review explores the causes, ecological consequences, and potential mitigation strategies for HABs. Effective monitoring and detection methods, including satellite remote sensing, molecular biotechnology, and artificial intelligence-driven predictive models, offer promising avenues for early intervention. Sustainable management strategies such as nutrient load reductions, bioremediation, and regulatory policies can help mitigate the adverse effects of HABs. Public awareness and community involvement also play a crucial role in preventing and managing HAB events by promoting responsible agricultural practices, reducing waste discharge, and supporting conservation efforts. By examining existing literature and case studies, this study underscores the urgent need for comprehensive and interdisciplinary approaches to regulate HABs. Full article

This study involved covering naval steel samples with a biocide-free, innovative antifouling coating, which were subsequently immersed in either artificial seawater or a Greek maritime environment for durations ranging from 1 to 50 weeks. The objective was to assess the efficacy of the coating as an anticorrosion and antifouling barrier on the steel samples. Non-coated samples were analyzed alongside the coated samples for comparative purposes. The findings indicate that a reduction in coating thickness during static immersion in laboratory settings leads to the removal of precipitated corrosion products, exposing a fresh layer of “pristine” coating. This layer decreases the corrosion rate by almost 90% throughout extended immersion durations. The efficacy of the coating is validated through trials conducted in natural maritime environments, demonstrating an operational performance of 99% for the coated samples after 50 weeks of continuous exposure to seawater. In fact, the coated samples showed only soft fouling, in contrast to the uncoated samples which were characterized by a strong presence of hard fouling within a short period of time after immersion. Full article

The decommissioning and dismantling of nuclear research reactors can lead to a large amount of low- and intermediate-level radioactive waste. For repositories, the materials must be kept confined and safety must be ensured for extended time spans. Waste is encapsulated in concrete, which leads to alkaline conditions with pH values of 12 and higher. This can be advantageous for some radionuclides due to their precipitation at high pH. For other materials, such as reactive metals, however, it can be disadvantageous because it might foster their corrosion. The Studsvik R2 research reactor contained an AlMg3.5 alloy with a composition close to that of commercial Al5154 for its core internals and the reactor tank. Aluminum corrosion is known to start rapidly due to the formation of an oxidation layer, which later functions as natural protection for the surface. The corrosion can lead to pressure build-up through the accompanied production of hydrogen gas. This can lead to cracks in the concrete, which can be pathways for radioactive nuclides to migrate and must therefore be prevented. In this study, unirradiated rod-shaped samples were cut from the same material as the original reactor tank manufacture. They were embedded in concrete with elevated water–cement ratios of 0.7 compared to regular commercial concrete (ca. 0.45) to ensure water availability throughout all of the experiments. The sample containers were stored in pressure vessels with attached high-definition pressure gauges to read the hydrogen-induced pressure build-up. A second set of samples were exposed in simplified artificial cement–water to study similarities in corrosion characteristics between concrete and cement–water. Additionally, the samples were exposed to concrete and cement–water in free-standing sample containers for deconstructive examinations. In concrete, the corrosion rates started extremely high, with values of more than 10,000 µm/y, and slowed down to less than 500 µm/y after 2000 h, which resulted in visible channels inside the concrete. In the cement–water, the samples showed similar behavior after early fluctuations, most likely caused by the surface coverage of hydrogen bubbles. These trends were further supported by mass loss evaluations. Full article

Rapid rises in water levels due to heavy rainfall can lead to the accumulation of floating debris, posing significant challenges for both water quality and resource management. However, real-time monitoring of floating debris remains difficult due to the discrepancy between meteorological conditions and the timing of debris accumulation. To address this limitation, this study proposes an amplitude change detection (ACD) model based on time-series synthetic aperture radar (SAR) imagery, which is less affected by weather conditions. The model statistically distinguishes floating debris from open water based on their differing scattering characteristics. The ACD approach was applied to 18 pairs of Sentinel-1 SAR images acquired over Daecheong Reservoir from June to September 2024. A stringent type I error threshold (α < 1 × 10⁻⁸) was employed to ensure reliable detection. The results revealed a distinct cumulative effect, whereby the detected debris area increased immediately following rainfall events. A positive correlation was observed between 10-day cumulative precipitation and the debris-covered area. For instance, on 12 July, a floating debris area of 0.3828 km² was detected, which subsequently expanded to 0.4504 km² by 24 July. In contrast, on 22 August, when rainfall was negligible, no debris was detected (0 km²), indicating that precipitation was a key factor influencing the detection sensitivity. Comparative analysis with optical imagery further confirmed that floating debris tended to accumulate near artificial barriers and narrow channel regions. Overall, this study demonstrates that this spatial pattern suggests the potential to use detection results to estimate debris transport pathways and inform retrieval strategies. Full article

In 1900, Francis Herbert Jennison’s book The Manufacture of Lake Pigments from Artificial Colours was published in London. In the early 20th century, the technical literature focussing on synthetic dyes mainly dealt with their use for dyeing. Conversely, the literature on lake pigment manufacture is less comprehensive, and Jennison’s publication was the first monograph on this topic. His book comprises descriptions of the dyes, substrates, and various methods for lake making. Practical examples complete the work: sixteen colour plates with original samples of lake pigments showcase the practical effect on colour of the different dyes and preparation methods. Herein, we present an overview of the context of Jennison’s research and delve into a selection of formulations. Green lake pigment plates were sampled and analysed by liquid chromatography coupled with spectroscopic and spectrometric detectors and by X-ray fluorescence spectroscopy to correlate the chemical composition with the recipes reported in the book. Seldom or no longer used and unexplored historical dyes were detected, along with polyphenolic compounds possibly used as precipitating agents in lake pigment formulations. Moreover, the examination of two different editions of the Jennison manuscript (i.e., the English and German books) revealed different chemical profiles corresponding to the same lake pigment formulation. This emphasizes the significance of Jennison’s book, confirming how understanding of early formulations is needed to elucidate the later ones. Full article

Sustained reductions in terrestrial water storage (TWS) have been observed globally using Gravity Recovery and Climate Experiment (GRACE) satellite data since 2002. However, the underlying mechanisms remain incompletely understood due to limited record lengths and data discontinuity. Recently, explainable artificial intelligence (XAI) has provided robust tools for unveiling dynamics in complex Earth systems. In this study, we employed a deep learning technique (Long Short-Term Memory network, LSTM) to reconstruct global TWS dynamics, filling gaps in the GRACE record. We then utilized the Local Interpretable Model-agnostic Explanations (LIME) method to uncover the underlying mechanisms driving observed TWS reductions. Our results reveal a consistent decline in the global mean TWS over the past 22 years (2002–2024), primarily influenced by precipitation (17.7%), temperature (16.0%), and evapotranspiration (10.8%). Seasonally, the global average of TWS peaks in April and reaches a minimum in October, mirroring the pattern of snow water equivalent with approximately a one-month lag. Furthermore, TWS variations exhibit significant differences across latitudes and are driven by distinct factors. The largest declines in TWS occur predominantly in high latitudes, driven by rising temperatures and significant snow/ice variability. Mid-latitude regions have experienced considerable TWS losses, influenced by a combination of precipitation, temperature, air pressure, and runoff. In contrast, most low-latitude regions show an increase in TWS, which the model attributes mainly to increased precipitation. Notably, TWS losses are concentrated in coastal areas, snow- and ice-covered regions, and areas experiencing rapid temperature increases, highlighting climate change impacts. This study offers a comprehensive framework for exploring TWS variations using XAI and provides valuable insights into the mechanisms driving TWS changes on a global scale. Full article

Groundwater nitrate (NO₃⁻) contamination has emerged as a critical global environmental issue, posing serious human health risks. This study systematically investigated the hydrochemical processes, sources of NO₃⁻ pollution, the impact of land use on NO₃⁻ pollution, and drinking water safety in an urban area of southwestern China. Thirty-one groundwater samples were collected and analyzed for major hydrochemical parameters and dual isotopic composition of NO₃⁻ (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻). The groundwater samples were characterized by neutral to slightly alkaline nature, and were dominated by the Ca-HCO₃ type. Hydrochemical analysis revealed that water–rock interactions, including carbonate dissolution, silicate weathering, and cation exchange, were the primary natural processes controlling hydrochemistry. Additionally, anthropogenic influences have significantly altered NO₃⁻ concentration. A total of 19.35% of the samples exceeded the Chinese guideline limit of 20 mg/L for NO₃⁻. Isotopic evidence suggested that primary sources of NO₃⁻ in groundwater include NH₄⁺-based fertilizer, soil organic nitrogen, sewage, and manure. Spatial distribution maps indicated that the spatial distribution of NO₃⁻ concentration correlated strongly with land use types. Elevated NO₃⁻ levels were observed in areas dominated by agriculture and artificial surfaces, while lower concentrations were associated with grass-covered ridge areas. The unabsorbed NH₄⁺ from nitrogen fertilizer entered groundwater along with precipitation and irrigation water infiltration. The direct discharge of domestic sewage and improper disposal of livestock manure contributed substantially to NO₃⁻ pollution. The nitrogen fixation capacity of the grassland ecosystem led to a relatively low NO₃⁻ concentration in the ridge region. Despite elevated NO₃⁻ and F⁻ concentrations, the entropy weighted water quality index (EWQI) indicated that all groundwater samples were suitable for drinking. This study provides valuable insights into NO₃⁻ source identification and hydrochemical processes across varying land-use types. Full article