H2O extraction from remote icy lunar regolith using concentrated irradiation was investigated under high-vacuum and low-temperature conditions. The thermal sublimation of H2O(s) from packed beds of lunar regolith simulants was quantified with and without an indirect solar receiver for average concentrated irradiations of 37.06 f 2.66 and 74.62 f 3.57 kW/m2. The indirect solar receiver increased sublimation by an average of 18.7 % f 10.4 %, despite slower heating rates due to its increased thermal mass. Different average concentrated irradiations affected the heating rates and thermal gradients within the packed bed, but the impact on overall sublimation was not statistically significant. An inverse relationship between heating rates and normalized sublimation was also observed, where rapid sublimation near the heating elements led to the formation of a desiccated layer of regolith, which behaved as a thermal insulator and further limited heat transfer, reducing the sublimation efficiency. These findings provide key insights for optimizing in-situ resource utilization technologies, contributing to the development of efficient methods for extracting H2O from lunar regolith, which is essential for sustainable space exploration.

Soil liquefaction is a major contributor to earthquake damage. Evaluating the potential for liquefaction by conventional experimental or empirical methods is both time-intensive and laborious. Utilizing a machine learning model capable of precisely forecasting liquefaction potential might diminish the time, effort, and expenses involved. This research introduces an innovative predictive model created in three phases. Initially, correlation analysis determines essential elements affecting liquefaction. Secondly, predictions are produced using Convolutional Neural Networks (CNN) and Deep Belief Networks (DBN), verified by K-fold cross-validation to guarantee resilience. Third, Ant Colony Optimization (ACO) improves outcomes by increasing convergence efficiency and circumventing local minima. The suggested EC + ACO model substantially surpassed leading approaches, such as SVM-GWO, RF-GWO, and Ensemble Classifier-GA, attaining a very low False Negative Rate (FNR) of 2.00 % when trained on 90 % of the data. A thorough performance evaluation shown that the model achieved a cost value of 1.133 % by the 40th iteration, exceeding the performance of other models such SVMGWO (1.412 %), RF-GWO (1.305 %), and Biogeography Optimized-Based ANFIS (1.7439 %). The model exhibited significant improvements in convergence behavior, with a steady decline in cost values, especially between the 20th and 50th iterations. Additional validation using empirical data from the Tohoku-oki, Great East Japan earthquake substantiated the EC + ACO model's enhanced accuracy and dependability in mirroring observed results. These findings underscore the model's resilience and efficacy, providing a dependable method for forecasting soil liquefaction and mitigating its seismic effects.

Thorium extraction techniques, such as solvent extraction from monazite and electrosorption techniques from water leach purification (WLP) of radioactive waste residues, are important for thorium recovery, particularly in Malaysia. Despite their importance, previous studies have largely overlooked critical issues like radioactive hazards, human health risks, and environmental impacts associated with advanced thorium extraction methods. This study addresses these gaps by quantifying the environmental impact associated with solvent extraction and electrosorption techniques using a life cycle assessment (LCA) framework to compare environmental indicators for thorium recovery from monazite ore and WLP residues. The LCA was conducted from cradle to gate, incorporating inventory data from the Ecoinvent database 3 and SimaPro software version 9, with inputs of raw material extraction, transportation, energy consumption, and chemical uses. Emissions into air, water, and soil were quantified across all processing phases. The LCA midpoint findings reveal that thorium disulfate in monazite processing is the key contributor to global warming, producing 45 kg CO2-eq, whereas transportation and electricity consumption also considerably affect emissions, contributing 25.07 kg CO2-eq and 26.17 kg CO2-eq, respectively. Comparative analysis of midpoint indicators showed that solvent extraction had a more significant environmental impact than electrosorption in the context of human carcinogenic toxicity, freshwater ecotoxicity, and marine ecotoxicity. The damaged assessment highlighted endpoint indicators that monazite processing had a higher impact than WLP on human health (0.0364-0.0016 DALY), ecosystems (0.0016-0.0005 species & sdot;yr), and resources (0.0012-0.0005 USD, 2013), primarily due to the use of chemicals and emissions. Our study shows that electrosorption from WLP demonstrates superior environmental sustainability compared with solvent extraction from monazite, positioning it a more viable and efficient approach for radioactive waste treatment.

Cyst nematodes, some of the most important plantparasitic nematodes globally, cause major damage to Chinese cabbage and soybean plants in Korea. Cysts are commonly used for cyst nematode bioassays because many eggs are included inside cyst. Traditionally, cysts are extracted from the soil using the paper strip method or the centrifugal flotation method (CFM) combined with sieving. The specific gravity of sugar solution (SGSS) is often used in the CFM; however, the efficiency of cyst extraction and egg hatching in the CFM has not been studied. In this study, we assessed the effects of SGSS in a specific gravity range of 1.15 to 1.30 in the CFM on the cyst extraction and egg hatching of clover cyst nematode (Heterodera trifolii) and sugar beet cyst nematode (H. schachtii). High SGSS in the CFM within the range of 1.15 to 1.30 was positively correlated with the extraction of more cysts. Egg-hatching rates were not different between SGSSs, indicating that SGSS did not directly affect egg-hatching rates. These results showed that the cysts of cyst nematodes can be efficiently extracted with high SGSS in the CFM.

Paddy soils undergo wet-dry cycles that greatly influence the behaviour and availability of nutrients, but also of potentially toxic elements (PTEs). This study assessed the quality of paddy soils (actively cultivated and abandoned) and rice (white, brown, and wild) produced in the Baixo Vouga Lagunar (BVL) region, central-north Portugal. Surface soils were analysed for physicochemical parameters and chemical compositions, alongside sequential selective chemical extraction to evaluate metal(loid) availability. Chemical analyses were also performed on interstitial- and irrigation waters, and rice grains. The BVL soils are very strongly to moderately acidic (pH = 4.4-5.8), with organic matter contents reaching up to 34%, and exhibit a wide range of electrical conductivity values. Abandoned rice fields generally show higher values of these parameters and evidence of saline water intrusion. Several sites showed As, Cu, Pb, and U concentrations exceeding Portuguese thresholds for agricultural soils. While Cu levels were similar in both cultivated and abandoned fields, the latter had higher contents of As, Pb, and U. A geogenic origin is envisaged for these metal(loid)s, though anthropogenic contributions cannot be excluded. Sequential selective chemical extraction showed that Pb and U are strongly associated with available fractions, whereas amorphous Fe-oxyhydroxides primarily support As and Cu. Nevertheless, porewaters and irrigation waters showed low concentrations of these PTEs, suggesting minimal mobilisation to water. Furthermore, translocation to rice grains was low, with concentrations well below European Commission limits, indicating that elevated PTEs in soils do not necessarily lead to toxic levels in rice, providing reassurance regarding food safety.

As offshore wind turbines approach the end of their operational lifespan, the decommissioning process is gaining increasing importance, which highlights the need to develop expertise in the marine operations involved in this task. This study focuses on simulating the vibratory extraction of a monopile foundation for an offshore wind turbine using a crane barge. A numerical model is developed to couple the dynamic behavior of the monopile with that of the barge. To accurately represent soil behavior during the extraction process, a high-fidelity finite element model is first established to calculate the soil resistance component under high-frequency cyclic loading generated by the vibro-hammer. Then, time domain numerical simulations of the monopile decommissioning process are carried out, and an external Dynamic Link Library (DLL) is developed to integrate this soil resistance force into the time-domain program. Additionally, the centrifugal force exerted by the vibro-hammer is incorporated into the model through a separate DLL. Time-domain simulations are performed to analyze the barge's motion and the tension in the lifting wire under calm water conditions. These results are then compared with simulations conducted under long-crested waves to provide a comprehensive understanding of the extraction process. This study also examines the effect of the dynamic positioning system on the barge's dynamic response and the tension in the lifting wire. The outcome of this paper contributes to a better understanding of the complex decommissioning process under marine environments.

Amylase has numerous applications in the processing food sector, including brewing, animal feed, baking, fruit juice manufacturing, starch syrups, and starch liquefaction. Practical applications have been the primary focus of recent research on novel properties of bacterial alpha-amylases. Many amylolytic-active bacterial isolates were obtained from samples of organic-rich, salinity-rich soil. Morphological and 16S rRNA gene sequence studies clearly revealed that the organism belongs to Bacillus sp. and was named Bacillus cereus strain GL2 (PP463909.1 (When pH 6.0, 45 degrees C, and 12 hours of incubation were met the optimal growth conditions for the strain produced the highest amount of alpha-amylase activity. B. cereus strain GL2 alpha-amylase isoenzyme was purified to homogeneity using Sephacryl (TM) S-200 chromatography and ammonium sulfate precipitation. The electrophoretic molecular weight of B. cereus alpha-amylase was 58 kDa. The optimal pH and temperature for measuring alpha-amylase activity were 50 degrees C and 6.0, respectively. alpha-Amylase did not change at 50 degrees C. The purified enzyme improves bread texture by reducing stiffness while improving cohesiveness and flexibility. Purified alpha-amylase was added to the flour, which improved the rheological properties and overall bread quality. As a result, the alpha-amylase from B. cereus strain GL2 can be used to promote bread-making.

The Narrow-Angle Cameras (NACs) onboard the Lunar Reconnaissance Orbiter Camera (LROC) capture lunar images that play a crucial role in current lunar exploration missions. Among these images, those of the Moon's permanently shadowed regions (PSRs) are highly noisy, obscuring the lunar topographic features within these areas. While significant advancements have been made in denoising techniques based on deep learning, the direct acquisition of paired clean and noisy images from the PSRs of the Moon is costly, making dataset acquisition expensive and hindering network training. To address this issue, we employ a physical noise model based on the imaging principles of the LROC NACs to generate noisy pairs of images for the Moon's PSRs, simulating realistic lunar imagery. Furthermore, inspired by the ideas of full-scale skip connections and self-attention models (Transformers), we propose a denoising method based on deep information convolutional neural networks. Using a dataset synthesized through the physical noise model, we conduct a comparative analysis between the proposed method and existing state-of-the-art denoising approaches. The experimental results demonstrate that the proposed method can effectively recover topographic features obscured by noise, achieving the highest quantitative metrics and superior visual results.

Water resources on the Moon are a critical component of international strategies for exploration of the solar system and space-based economic development. Liquid water is essential for human life support and propellant generation. Extreme Lunar conditions of near-vacuum and low temperature preclude the natural presence of liquid water; and they provide the thermodynamic context for water occurrence and its potential extraction. Ice crystals were observed by LCROSS and are inferred to reside in pore spaces of lunar regolith or at the surface in places. Any system proposed for lunar ice mining by induced sublimation needs to address potential vapor loss to the ambient near-vacuum; regolith cohesiveness; low regolith thermal conductivity; negligible sublimation rates below-200K; low rates of vapor advection-diffusion through porous regolith; and pressurization due to sublimation that causes redeposition. All of these obstacles have potential solutions with available technologies, but they must be designed within power availability constraints and with the potential to scale up to the resource needs of a growing space economy.

Escalating anthropogenic activities have caused heavy metal contamination in the environmental matrices. Due to their recalcitrant and toxic nature, their occurrence in high titers in the environment can threaten survival of biotic components. To take the edge off, remediation of metal-contaminated sites by phytoremediators that exhibit a potential to withstand heavy metal stress and quench harmful metals is considered an eco-sustainable approach. Despite the enormous potential, phytoremediation technique suffers a setback owing to high metal concentrations, occurrence of multiple pollutants, low plant biomass, and soil physicochemical status that affect plants at cellular and molecular levels, inducing morphological, physiological, and genetic alterations. Nevertheless, augmentation of soil with microorganisms can alleviate the challenge. A positive nexus between microbes, particularly plant growth-promoting microorganisms (PGPMs), and phytoremediators can prevent phytotoxicity and augment phytoremediation by employing strategies such as production of secondary metabolites, solubilization of phosphate, and synthesis of 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase and phytohormones. Microbes can mediate tolerance in plants by fortifying their antioxidant machinery, which maintains redox homeostasis and alleviates metal-induced oxidative damage in the plants. Associated microbes can also activate stress-responsive genes in plants and abridge metal-induced toxic effects. An in-depth exploration of the mechanisms employed by plant-associated microbes to trigger tolerance in phytoremediators is crucial for improving their phytoremediation potential and real-world applications. The present article attempts to comprehensively review these mechanisms that eventually facilitate the development of improved/new technology for soil ecosystem restoration.