Search Results (202)

Ensuring the safe landing of deep-sea mining vehicles (DSMVs) on soft seabed sediments is critical for the stability and operational reliability of subsea mineral extraction. However, deep-sea sediments, particularly in polymetallic nodule regions, are characterized by low shear strength, high compressibility, and rate-dependent behavior, posing significant challenges for full-scale experimental investigation and predictive modeling. To address these limitations, this study develops a high-fidelity finite element simulation framework based on the Coupled Eulerian–Lagrangian (CEL) method to model the landing and penetration process of full-scale DSMVs under various geotechnical conditions. To overcome the high computational cost of FEM simulations, a data-driven surrogate model using the random forest algorithm is constructed to predict the normalized penetration depth based on key soil and operational parameters. The proposed hybrid FEM–ML approach enables efficient multiparameter analysis and provides actionable insights into the complex soil–structure interactions involved in DSMV landings. This methodology offers a practical foundation for engineering design, safety assessment, and descent planning in deep-sea mining operations. Full article

The article presents research on concrete creep in bridge structures, focusing on the influence of concrete mix composition and the use of advanced rheological models with fractional-order derivatives. Laboratory tests were performed on nine mixes varying in blast furnace slag content (0%, 25%, and 75% of cement mass) and air-entrainment. The results were used to calibrate fractal rheological models—Kelvin–Voigt and Huet–Sayegh—where the viscous element was replaced with a fractal element. These models showed high agreement with experimental data and improved the accuracy of creep prediction. Comparison with Eurocode 2 revealed discrepancies up to 64%, especially for slag-free concretes used in prestressed bridge structures. The findings highlight the important role of mineral additives in reducing creep strains and the need to consider individual mix characteristics in design calculations. In the context of modern bridge construction technologies, such as balanced cantilever or incremental launching, reliable modeling of early-age creep is particularly important. The proposed modeling approach may enhance the precision of long-term structural behavior analyses and contribute to improved safety and durability of concrete infrastructure. Full article

Urban wetlands play a crucial role in maintaining ecological balance, carbon sequestration, and water purification. Sediments are key carriers for wetlands to store elements such as carbon, nitrogen, and phosphorus in the aquatic environment. This study analyzed different sediment layers of seven wetlands in Yangzhou, aiming to explore the relationship between physicochemical factors and microbial communities in wetland sediments, as well as to predict the functions of microbial communities. Functional prediction of microbial communities was conducted based on amplicon sequencing analysis, and the neutral community model was used to determine the formation and evolution process of microbial communities. The results showed that in three wetlands, namely Zhuyu Bay (ZYW), Luyang Lake (LYH), and Runyang Wetland (RYSD), the contents of carbon components (total carbon, total soluble carbon, microbial biomass carbon) in the 0–20 cm sediment layer were higher, while the carbon component contents in Baoying Lake (BYH) showed the opposite trend. Among them, the contents of total nitrogen, alkali-hydrolyzable nitrogen, total phosphorus, available phosphorus, total potassium, and available potassium in the 0–20 cm sediment layer of Runyang Wetland (RYSD) were significantly the highest. This indicates that in Runyang Wetland (RYSD), the 0–20 cm layer has more abundant carbon components and mineral nutrients compared to the 20–40 cm layer. Among the seven wetlands, it was found that the content of total potassium was all greater than 10 g/kg, which was much higher than the contents of total phosphorus and total nitrogen. Analysis of microbial communities revealed that the dominant archaeal phyla were Thaumarchaeota and Euryarchaeota, and the dominant bacterial phyla were Proteobacteria and Acidobacteria. The distribution of functional genes was mainly concentrated in Zhuyu Bay (ZYW) and Luyang Lake (LYH). Zhuyu Bay Wetland (ZYW) had potential advantages in light utilization function, and Luyang Lake (LYH) had potential advantages in carbon and nitrogen cycle functions. The assembly process of the archaeal community was mainly affected by stochastic processes, while the bacterial community was mainly affected by deterministic processes. However, water content, total phosphorus, and available potassium all had strong correlations with both archaeal and bacterial communities. The research results preliminarily reveal the connections between the physicochemical properties of sediments, microbial communities, and their potential functions in Yangzhou urban wetlands, providing an important scientific basis for the protection and management of wetland ecosystems. Full article

With the rapid increase in global lithium demand, the exploration of newly discovered lithium in the bauxite of the Wenshan area in southeastern Yunnan has become increasingly important. However, the current research on clay-type lithium in the Wenshan area has primarily focused on local exploration, and large-scale predictive metallogenic studies remain limited. To address this, this study utilized multi-source remote sensing data from ZY1-02D and ASTER, combined with ALOS 12.5 m DEM and Sentinel-2 imagery, to carry out remote sensing mineral identification, structural interpretation, and prospectivity mapping for clay-type lithium in the Wenshan area. This study indicates that clay-type lithium in the Wenshan area is controlled by NW, EW, and NE linear structures and are mainly distributed in the region from north of the Wenshan–Malipo fault to south of the Guangnan–Funing fault. High-value areas of iron-rich silicates and iron–magnesium minerals revealed by ASTER data indicate lithium enrichment, while montmorillonite and cookeite identification by ZY1-02D have strong indicative significance for lithium. Field verification samples show the highest Li₂O content reaching 11,150 μg/g, with six samples meeting the comprehensive utilization criteria for lithium in bauxite (Li₂O ≥ 500 μg/g) and also showing an enrichment of rare earth elements (REEs) and gallium (Ga). By integrating stratigraphic, structural, mineral identification, geochemical characteristics, and field verification data, ten mineral exploration target areas were delineated. This study validates the effectiveness of remote sensing technology in the exploration of clay-type lithium and provides an applicable workflow for similar environments worldwide. Full article

Mining and analyzing information from multiple sources—such as geophysics and geochemistry—is a key aspect of big data-driven mineral prediction. Clustering, which groups large datasets based on distance metrics, is an essential method in multidimensional data analysis. The Two-Step Clustering (TSC) approach offers advantages by handling both categorical and continuous variables and automatically determining the optimal number of clusters. In this study, we applied the TSC method to mineral prediction in the northeastern margin of the Jiaolai Basin by: (i) converting residual gravity and magnetic anomalies into categorical variables using Ward clustering; and (ii) transforming 13 stream sediment elements into independent continuous variables through factor analysis. The results showed that clustering is sensitive to categorical variables and performs better with fewer categories. When variables share similar distribution characteristics, consistency between geophysical discretization and geochemical boundaries also influences clustering results. In this study, the (3 × 4) and (4 × 4) combinations yielded optimal clustering results. Cluster 3 was identified as a favorable zone for gold deposits due to its moderate gravity, low magnetism, and the enrichment in F1 (Ni–Cu–Zn), F2 (W–Mo–Bi), and F3 (As–Sb), indicating a multi-stage, shallow, hydrothermal mineralization process. This study demonstrates the effectiveness of combining Ward clustering for variable transformation with TSC for the integrated analysis of categorical and numerical data, confirming its value in multi-source data research and its potential for further application. Full article

The intentional yaw offset of wind turbines has shown potential to redirect wakes, enhancing overall plant power production, but it may increase fatigue loading on turbine components. This study analyzed fatigue loads on the NREL 5 MW reference wind turbine under varying yaw offsets using blade element momentum theory, dynamic blade element momentum, and the converging Lagrange filaments vortex method, all implemented in OpenFAST. Simulations employed yaw angles from −40° to 40°, with turbulent inflow generated by TurbSim, an OpenFAST tool for realistic wind conditions. Fatigue loads were calculated according to IEC 61400-1 design load case 1.2 standards, using thirty simulations per yaw angle across five wind speed bins. Damage equivalent load was evaluated via rainflow counting, Miner’s rule, and Goodman correction. Results showed that the free vortex method, by modeling unsteady aerodynamic forces, yielded distinct differences in damage equivalent load compared to the blade element method in yawed conditions. The free vortex method predicted lower damage equivalent load for the low-speed shaft bending moment at negative yaw offsets, attributed to its improved handling of unsteady effects that reduce load variations. Conversely, for yaw offsets above 20°, the free vortex method indicated higher damage equivalent for low-speed shaft torque, reflecting its accurate capture of dynamic inflow and unsteady loading. These findings highlight the critical role of unsteady aerodynamics in fatigue load predictions and demonstrate the free vortex method’s value within OpenFAST for realistic damage equivalent load estimates in yawed turbines. The results emphasize the need to incorporate unsteady aerodynamic models like the free vortex method to accurately assess yaw offset impacts on wind turbine component fatigue. Full article

Additive manufacturing reshapes concrete construction, yet routine strength verification of printed elements still depends on destructive core sampling. This study evaluates whether standard 70 mm cubes—corrected by a single factor—can provide an equally reliable measure of in situ compressive strength. Five Portland-cement mixes, with and without ash-slag techno-mineral filler, were extruded into wall blocks on a laboratory 3D printer. For each mix, the compressive strengths of the cubes and ∅ 28 mm drilled cores were measured at 7, 14 and 28 days. The core strengths were consistently lower than the cube strengths, but their ratios remained remarkably stable: the transition coefficient clustered between 0.82 and 0.85 (mean 0.83). Ordinary least-squares regression of the pooled data produced the linear relation ${\widehat{R}}_{core}$ [MPa] = 0.97 ${\widehat{R}}_{cube}$ − 4.9, limiting the prediction error to less than 2 MPa (under 3% across the 40–300 MPa range) and outperforming more complex machine-learning models. Mixtures containing up to 30% ash-slag filler maintained structural-grade strength while reducing clinker demand, underscoring their sustainability potential. The results deliver a simple, evidence-based protocol for non-destructive strength assessment of 3D-printed concrete and provide quantitative groundwork for future standardisation of quality-control practices in additive construction. Full article

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care. Full article

Microelement deficiencies, often termed “hidden hunger”, represent a significant global health challenge. Optimal human health relies on adequate dietary intake of essential microelements, including selenium (Se), zinc (Zn), copper (Cu), boron (B), manganese (Mn), molybdenum (Mo), iron (Fe), nickel (Ni), and chlorine (Cl). In recent years, there has been a growing focus on vitality and longevity, which are closely associated with the sufficient intake of essential microelements. This review focuses on these nine elements, whose bioavailability in the food chain is critically determined by their geochemical behavior in soils. There is a necessity for an understanding of the sources, soil–plant transfer, and plant uptake mechanisms of these microelements, with particular emphasis on the influence of key soil properties, including pH, redox potential, organic matter content, and mineral composition. There is a dual challenge of microelement deficiencies in agricultural soils, leading to inadequate crop accumulation, and the potential for localized toxicities arising from anthropogenic inputs or geogenic enrichment. A promising solution to microelement deficiencies in crops is biofortification, which enhances nutrient content in food by improving soil and plant uptake. This strategy includes agronomic methods (e.g., fertilization, soil amendments) and genetic approaches (e.g., marker-assisted selection, genetic engineering) to boost microelement density in edible tissues. Moreover, emphasizing the need for advanced predictive modeling techniques, such as ensemble learning-based digital soil mapping, enhances regional soil microelement management. Integrating machine learning with digital covariates improves spatial prediction accuracy, optimizes soil fertility management, and supports sustainable agriculture. Given the rising global population and the consequent pressures on agricultural production, a comprehensive understanding of microelement dynamics in the soil–plant system is essential for developing sustainable strategies to mitigate deficiencies and ensure food and nutritional security. This review specifically focuses on the bioavailability of these nine essential microelements (Se, Zn, Cu, B, Mn, Mo, Fe, Ni, and Cl), examining the soil–plant transfer mechanisms and their ultimate implications for human health within the soil–plant–human system. The selection of these nine microelements for this review is based on their recognized dual importance: they are not only essential for various plant metabolic functions, but also play a critical role in human nutrition, with widespread deficiencies reported globally in diverse populations and agricultural systems. While other elements, such as cobalt (Co) and iodine (I), are vital for health, Co is primarily required by nitrogen-fixing microorganisms rather than directly by all plants, and the main pathway for iodine intake is often marine-based rather than soil-to-crop. Full article

In this study, we propose a self-adaptive weighted multi-mineral inversion model (SQP_AW) based on Sequential Quadratic Programming (SQP) and the Adam optimization algorithm for the accurate evaluation of mineral content in carbonate reservoir rocks, addressing the high costs of traditional experimental methods and the strong parameter dependence in geophysical inversion. The model integrates porosity curves (compensated density, compensated neutron, and acoustic time difference), elastic modulus parameters (shear and bulk moduli), and nuclear magnetic porosity data for the construction of a multi-dimensional linear equation system, with calibration coefficients derived from core X-ray diffraction (XRD) data. The Adam algorithm dynamically optimizes the weights, solving the overdetermined equation system. We applied the method to the Asmari Formation in the M oilfield in the Middle East with 40 core samples for calibration, achieving a 0.91 fit with the XRD data. For eight additional uncalibrated samples from Well A, the fit reaches 0.87. With the introduction of the elastic modulus and nuclear magnetic porosity, the average relative error in mineral content decreases from 9.45% to 6.59%, and that in porosity estimation decreases from 8.1% to 7.1%. The approach is also scalable to elemental logging data, yielding inversion precision comparable to that of commercial software. Although the method requires a complete set of logging data and further validation of regional applicability for weight parameters, in future research, transfer learning and missing curve prediction could be incorporated to enhance its practical utility. Full article

In oil and gas field development, the highly mineralized formation water environment often leads to casing corrosion, significantly reducing its collapse strength and service life. To investigate the corrosion characteristics and mechanical degradation of casings in formation water, this study combines high-temperature high-pressure (HTHP) corrosion experiments with finite element analysis to examine the influence of corrosion pit geometry and volume loss on casing collapse strength. Based on experimental data and simulation results, the safe service life of casing under various conditions is also predicted. The results show that greater material loss due to corrosion leads to lower collapse strength. Under the same volume loss, cylindrical pits cause the most severe reduction in collapse strength, while semi-ellipsoidal pits result in the least degradation. Elevated temperature and pressure significantly accelerate corrosion and further reduce mechanical performance. Under service conditions of 20 MPa–80 °C, 11 MPa–50 °C, and 2 MPa–20 °C, the predicted safe service life of the casing is 4.84, 7.57, and 11.61 years, respectively. The approach proposed in this study provides a reference for the evaluation of collapse resistance and service life prediction of casings exposed to formation water environments. Full article

Spinach (Spinacia oleracea L.), a globally consumed, nutrient-dense vegetable, contains diverse vitamins and minerals. However, elevated temperatures can constrain yield by interrupting leaf development and photosynthetic efficiency. The mitochondrial transcription termination factor (mTERF) family, which regulates organellar gene expression, plays crucial roles in plant growth and photosynthetic regulation. Thus, characterization of the spinach mTERF (SomTERF) family is critical for elucidating thermotolerance mechanisms in this crop. In this study, we systematically identified 31 SomTERF genes from the spinach genome, which are distributed across five chromosomes and nine unassembled genomic scaffolds. Subcellular localization predictions indicated that these proteins predominantly target chloroplasts and mitochondria. Conserved domain analyses confirmed that all SomTERF proteins possess canonical mTERF domains and ten conserved motifs. Phylogenetic clustering segregated these proteins into nine distinct subgroups (I–IX), with significant divergence observed in gene copy numbers among subgroups. Cis-element screening identified an abundance of heat-, cold-, and hormone-responsive motifs within SomTERF promoter regions. Notably, seven members (including SomTERF5) exhibited pronounced enrichment of heat shock elements (HSEs). Organ-specific expression profiling revealed preferential leaf expression of these seven genes. Comparative RT-qPCR in heat-sensitive (Sp73) and heat-tolerant (Sp75) cultivars under thermal stress demonstrated genotype-dependent expression dynamics. Functional validation of SomTERF5 was achieved through cloning, and transgenic Arabidopsis overexpressing SomTERF5 showed significantly enhanced thermotolerance, as evidenced by improved survival rates following heat treatment. Yeast two-hybrid (Y2H) assays further revealed physical interaction between SomTERF5 and SopTAC2. This study provides a comprehensive foundation for understanding mTERF-mediated developmental regulation and advanced molecular breeding strategies for developing heat-resilient spinach varieties. Full article

The Axi gold deposit, which is located in the Tulasu Basin of the West Tianshan orogenic belt in Northwest China, features vein-type ore bodies hosted in radial structural fractures formed due to volcanic activity. The deposit experienced three distinct mineralization stages: Stage I, characterized by the microcrystalline quartz–pyrite crust; Stage II, characterized by quartz–sulfide–native gold veins; and Stage III, characterized by quartz–carbonate veins. Fluid inclusion studies have identified four types of inclusions: pure vapor, vapor-rich, liquid-rich, and pure liquid. The number of vapor-rich inclusions decreases when moving from Stage I to Stage III, whereas the number of liquid-rich inclusions increases. The fluid temperature gradually decreases from 178–225 °C in Stage I to 151–193 °C in Stage II and further to 123–161 °C in Stage III, whereas the fluid salinity decreases slightly from 2.1%–5.1% wt.% NaCl eqv to 1.4%–4.6% wt.% NaCl eqv and finally to 0.5%–3.7% wt.% NaCl eqv. As suggested by the results of the oxygen, hydrogen, and carbon isotope analyses, the ore-forming fluids were primarily meteoric water. Sulfur isotopic compositions indicate a single deep mantle source. The lead isotopic compositions closely resemble those of Dahalajunshan Formation volcanic rocks, indicating that these rocks were the primary source of the ore-forming material. In addition, gold mineralization formed in a Devonian–Early Carboniferous volcanic arc environment. Element enrichment was mainly caused by the circulation of heated meteoric water through the volcanic strata, while fluid boiling and water–rock interactions were the main mechanisms driving element precipitation. The integrated model developed in this study underscores the intricate interplay between volcanic processes and meteoric fluids during the formation of the Axi gold deposit, offering a robust framework for an understanding of the formation processes and enhancing the predictive exploration models in analogous geological settings. Full article

The welded joints in steel bridges have a complicated structure, and their fatigue life is mainly determined by the real stress under the coupling effect of vehicle load stress, as well as weld-induced residual stress and its relaxation. Traditional fatigue analysis methods are inadequate for effectively accounting for weld-induced residual stress and its relaxation, resulting in a significant discrepancy between the predicted fatigue life and the actual fatigue cracking time. A fatigue damage assessment model of welded joints was developed in this study, considering weld-induced residual stress and its relaxation under vehicle load stress. A multi-scale finite element model (FEM) for vehicle-induced coupled analysis was established to investigate the weld-induced initial residual stress and its relaxation effect associated with cyclic bend fatigue due to vehicles. The fatigue damage assessment, considering the welding residual stress and its relaxation, was performed based on the S–N curve model from metal fatigue theory and Miner’s linear damage theory. Based on this, the impact of variations in traffic load on fatigue life was forecasted. The results show that (1) the state of tension or compression in vehicle load stress notably impacts the residual stress relaxation effect observed in welded joints, of which the relaxation magnitude of the von Mises stress amounts to 81.2% of the average vehicle load stress value under tensile stress working conditions; (2) the predicted life of deck-to-rib welded joints is 28.26 years, based on traffic data from Jiangyin Bridge, which is closer to the monitored fatigue cracking life when compared with the Eurocode 3 and AASHTO LRFD standards; and (3) when vehicle weight and traffic volume increase by 30%, the fatigue life significantly drops to just 9.25 and 12.13 years, receptively. Full article

The chemical composition of apatite has been utilized as an indicator of magmatic fertility related to tungsten mineralization in skarn systems. In this study, we compiled 5776 apatite trace element data from 374 intrusions, along with records indicating magmatic fertility. Then we trained and validated machine learning (ML) models, specifically support vector machine (SVM) and random forests (RF), to classify magmatic fertility based on apatite chemistry in igneous rocks. RF model achieved high classification accuracies (~93%) on the test dataset, demonstrating that employing ML approaches to distinguish apatite derived from fertile versus barren magmas is feasible and effective. Furthermore, we optimized classification thresholds to maximize the model’s predictive accuracy for identifying potentially fertile magmas. Feature-importance analysis of the machine learning classifier shows that elevated La, Yb, and Mn, together with depleted Sr, Y, Gd, and Tb, constitute the most diagnostic elemental signatures of magmatic fertility. As a case study, we applied our trained ML model to predict the magmatic fertility of apatite samples from the Nanling Range (southern China’s largest skarn-type tungsten mineralization province). Benefiting from the application of GAN-based techniques to address sample imbalance, our ML models can effectively identify tungsten-mineralized favorable skarn areas. Additionally, the visualization technique t-distributed stochastic neighbor embedding (t-SNE) was employed to validate and assess classification outcomes. Results showed clear separation between fertile and barren categories within the reduced 3D space. Our findings emphasize apatite as a sensitive indicator mineral for granite-related magmatic fertility and metallogenesis, underscoring its significant potential in mineral exploration. Finally, we provide a convenient prediction software for magmatic fertility based on a machine learning model utilizing apatite trace element compositions. Full article