Search Results (564)

FeCrAl exhibits excellent resistance to high temperatures, corrosion, and irradiation, making it a prime candidate material for accident-tolerant fuel (ATF) cladding. This study investigates the high-temperature creep behavior of FeCrAl alloys with grain sizes of 12.0 μm and 9.9 μm under temperatures ranging from 450 °C to 650 °C and applied stresses between 75 and 200 MPa. The texture, grain morphology, grain orientation, and dislocation density of FeCrAl were characterized by electron backscatter diffraction (EBSD). The results indicate that temperature, applied stress, and grain size are the primary factors governing high-temperature creep behavior. The material texture showed no significant difference before and after creep. Large grains tend to engulf smaller ones during the creep process at lower temperatures and stresses, reducing the proportion of low-angle grain boundaries (LAGBs). In contrast, at higher temperatures or under higher stress, dislocations proliferate within grains, leading to a significant increase in the number of LAGBs. As the applied stress increases, the dominant creep mechanism tends to convert from grain boundary sliding to dislocation motion. Moreover, higher temperatures or smaller grain sizes lower the critical stress required to activate dislocation motion and significantly increase dislocation density, severely degrading the creep resistance. Full article

Plant microbiomes are vital for the growth and health of their host. Tree-associated microbiomes are shaped by multiple factors, of which the host is one of the key determinants. Whether different host genotypes affect the structure and diversity of the tissue-associated microbiome and how specific taxa enriched in different tree tissues are not yet well illustrated. Chinese fir (Cunninghamia lanceolata) is an important tree species for both economy and ecosystem in the subtropical regions of Asia. In this study, we investigated the tissue-specific fungal community structure and diversity of nine different Chinese fir genotypes (39 years) grown in the same field. With non-metric multidimensional scaling (NMDS) analysis, we revealed the divergence of the fungal community from rhizosphere soil (RS), fine roots (FRs), and thick roots (TRs). Through analysis with α-diversity metrics (Chao1, Shannon, Pielou, ACE, Good‘s coverage, PD-tree, Simpson, Sob), we confirmed the significant difference of the fungal community in RS, FR, and TR samples. Yet, the overall fungal community difference was not observed among nine genotypes for the same tissues (RS, FR, TR). The most abundant fungal genera were Russula in RS, Scytinostroma in FR, and Subulicystidium in TR. Functional prediction with FUNGuild analysis suggested that ectomycorrhizal fungi were commonly enriched in rhizosphere soil, while saprotroph–parasite and potentially pathogenic fungi were more abundant in root samples. Specifically, genotype N104 holds less ectomycorrhizal and pathogenic fungi in all tissues (RS, FR, TR) compared to other genotypes. Additionally, significant correlations of several endophytic fungal taxa (Scytinostroma, Neonothopanus, Lachnum) with the growth traits (tree height, diameter, stand volume) were observed. This addresses that the interaction between tree roots and the fungal community is a reflection of tree growth, supporting the “trade-off” hypothesis between growth and defense in forest trees. In summary, we revealed tissue-specific, as well as host genotype-specific and genotype-common characters of the structure and functions of their fungal communities. Full article

Semi-supervised object detection (SSOD) in remote sensing is challenged by the accumulation of pseudo-label errors in complex scenes with dense objects and high intra-class variability. While teacher–student frameworks enable learning from unlabeled data, erroneous pseudo-labels such as false positives and missed detections can be reinforced over time, which degrades model performance. To address this issue, we propose the Error-Mitigation Teacher (EMT), a unified framework designed to suppress error propagation during SSOD training. EMT consists of three lightweight modules. First, the Adaptive Pseudo-Label Filtering (APLF) module removes noisy pseudo boxes via a second-stage RCNN and adjusts class-specific thresholds through dynamic confidence filtering. Second, the Confidence-Based Loss Reweighting (CBLR) module reweights training loss by evaluating the teacher model’s ability to reconstruct its own pseudo-labels, using the resulting loss as an indicator of label reliability. Third, the Enhanced Supervised Learning (ESL) module improves class-level balance by adjusting supervised loss weights according to pseudo-label statistics. EMT demonstrates consistent performance gains over representative state-of-the-art SSOD methods on DOTA, DIOR, and SSDD datasets. Notably, EMT achieves a 2.9% absolute mAP₅₀ improvement on DIOR using only 10% of labeled data, without incurring additional inference cost. These results highlight EMT’s effectiveness in improving SSOD for remote sensing. Full article

This research proposes optimizing the thermal pattern in the sintering bed by manipulating the air supply. The impact of the air supply on the distribution of heat in the upper and lower layers of the material bed is investigated based on a numerical simulation model. An optimized air supply scheme is proposed to enhance the thermal distribution of the sintering bed. The simulation results suggest that decreasing the air supply during sintering in the upper layer leads to an increase in bed temperature and an extension of the melting zone thickness from 5 mm to 16 mm. Similarly, reducing the air supply during sintering of the lower layer prevents over-melting of the sintering material by reducing heat accumulation. However, both decrease the speed of vertical sintering. To optimize the sintering process, it is suggested to decrease the air supply during the early and late stages and increase it during the middle stage. This optimized air supply leads to a uniform temperature distribution, with a 30 °C decrease in the gap between the highest temperatures. Additionally, the melting zone thickness in the early sintering stage increases from 0 mm to 14 mm, and the average vertical sintering speed remains comparable. Full article

Ammonia, as a toxic and corrosive gas, is widely present in industrial emissions, agricultural activities, and disease biomarkers. Detecting ammonia is of vital importance to environmental safety and human health. Sensors based on MXene have become an effective means for detecting ammonia gas due to their unique hierarchical structure, adjustable surface chemical properties, and excellent electrical conductivity. This study reviews the latest progress in the use of MXene and its composites for the low-temperature detection of ammonia gas. The strategies for designing MXene composites, including heterojunction engineering, surface functionalization, and active sites, are introduced, and their roles in improving sensing performance are clarified. These methods have significantly improved the ability to detect ammonia, offering high selectivity, rapid responses, and ultra-low detection limits within the low-temperature range. Successful applications in fields such as industrial safety, food quality monitoring, medical diagnosis, and agricultural management have demonstrated the multi-functionality of this technology in complex scenarios. The challenges related to the material’s oxidation resistance, humidity interference, and cross-sensitivity are also discussed. This study aims to briefly describe the reasonable design based on MXene sensors, aiming to achieve real-time and energy-saving environmental and health monitoring networks in the future. Full article

Model-Based Systems Engineering (MBSE) has gained significant attention from both industry and academia as an effective approach to managing product complexity. Despite its progress, current MBSE concepts, tools, languages, and methodologies face notable challenges in industrial applications, particularly in addressing design variability, ensuring model consistency, and enhancing operational efficiency. Based on the authors’ industry observations and literature analysis, this paper identifies the primary limitations of traditional MBSE, and introduces MBSE 2.0, a next-generation evolution characterized by comprehensive, integrated, and intelligent features. Key enabling technologies, such as model governance, integrated design methods, and AI-enhanced system design, are explored in detail. Additionally, several preliminary explorations were introduced under the guidance of the MBSE 2.0 philosophy. This study introduces the MBSE 2.0 concept to stimulate discussion and guide future efforts in academia and industry, emphasizing key advancements and highlighting several key and pressing perspectives to alleviate current limitations in industrial practice. Full article

Grain boundary weakening in high-temperature environments significantly influences the fatigue crack growth mechanisms of nickel-based superalloys, introducing challenges in accurately predicting fatigue life. In this study, a dislocation-density-based crystal plasticity phase-field (CP–PF) model is developed to simulate the fatigue crack growth behavior of the GH4169 alloy under both room and elevated temperatures. Grain boundaries are explicitly modeled, enabling the competition between transgranular and intergranular cracking to be accurately captured. The grain boundary separation energy and surface energy, calculated via molecular dynamics simulations, are employed as failure criteria for grain boundary and intragranular material points, respectively. The simulation results reveal that under oxygen-free conditions, fatigue crack propagation at both room and high temperatures is governed by sustained shear slip, with crack advancement hindered by grains exhibiting low Schmid factors. When grain boundary oxidation is introduced, increasing oxidation levels progressively degrade grain boundary strength and reduce overall fatigue resistance. Specifically, at room temperature, oxidation shortens the duration of crack arrest near grain boundaries. At elevated service temperatures, intensified grain boundary degradation facilitates a transition in crack growth mode from transgranular to intergranular, thereby accelerating crack propagation and exacerbating fatigue damage. Full article

A sensitive method was developed for detecting diazepam residues in aquatic products using magnetic dispersive solid-phase extraction (MDSPE) coupled with ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). Samples extracted with 1% ammonia–acetonitrile were purified using synthesized Fe₃O₄@SiO₂-PSA nanoparticles via MDSPE before UPLC-MS/MS analysis. Separation was performed on a C₁₈ column with gradient elution using 0.1% formic acid–2 mM ammonium acetate/methanol. Detection employed positive electrospray ionization (ESI⁺) in multiple reaction monitoring (MRM) mode. Characterization confirmed Fe₃O₄@SiO₂-PSA’s mesoporous structure with excellent adsorption capacity and magnetic properties. The method showed good linearity (0.1–10 μg/L, r > 0.99) with an LOD and LOQ of 0.20 μg/kg and 0.50 μg/kg, respectively. Recoveries at 0.5–15.0 µg/kg spiking levels were 74.9–109% (RSDs 1.24–11.6%). This approach provides rapid, accurate, and high-precision analysis of diazepam in aquatic products, meeting regulatory requirements. Full article

As global climate change intensifies and energy crises deepen, photovoltaic (PV) applications in cities are increasingly garnering attention worldwide. In this context, retrofitting existing high-density residential areas with PV applications is becoming a focus of urban low-carbon development. As the most densely populated city in Western China, Chengdu is characterized by rapid development and high energy consumption. The widespread application of photovoltaic (PV) systems could significantly alleviate its energy consumption issues. This research investigated the PV application potentials of 27 non-residential areas in high-density residential areas in Chengdu, Sichuan Province from a design perspective and proposed design recommendations for PV applications in these spaces. In addition, this study analyzed urban morphological factors affecting the PV generation potential in non-residential areas through a Pearson correlation. The key factors influencing the PV application potential in these areas were building density (BD), non-residential area perimeter-to-area ratio (NBPAR), and maximum building height (H_max). This research aims to provide new strategies and methods for the low-carbon transformation of future urban high-density residential areas. Full article

Recently, China has become a hotspot for farming golden pompano (Trachinotus ovatus), a commercially valuable marine fish. The genetic mechanisms underlying ovarian development, particularly those regulated by insulin-like growth factors (IGFs), remain poorly understood. Existing research on T. ovatus has focused primarily on growth metrics, developmental stages, and immune responses, leaving a critical gap in knowledge regarding the hepatic regulatory pathways activated by IGFs. In this study, differentially expressed genes (DEGs) were detected through RNA sequencing (RNA-Seq) and associated pathways in response to IGF treatment. Comparisons between the IGF1, IGF2, and IGF3 treated groups and the control revealed 113 (46 upregulated, 67 downregulated), 637 (567 upregulated, 70 downregulated), and 587 DEGs (273 upregulated, 314 downregulated), respectively. KEGG enrichment analysis highlighted key pathways that may be linked to ovarian growth and development, including biotin metabolism, biosynthesis of amino acids, drug-cytochrome p450 pathways, MAPK signaling, estrogen signaling pathways, ECM receptor interaction, steroid biosynthesis, and ovarian steroidogenesis. These findings advance our understanding of hepatic metabolic regulation in golden pompano via the IGF system and provide actionable insights for optimizing aquaculture practices and selective breeding programs for this species. Full article

FeCrAl alloys have garnered considerable attention as candidate cladding materials for light water reactors due to their promising mechanical stability and irradiation resistance. However, the response characteristics of these alloys to irradiation and the associated mechanisms remain poorly understood. This study provides atomistic insights into irradiation-induced defect formation and microstructural evolution in polycrystalline FeCrAl. Using the LAMMPS molecular dynamics code, displacement cascades were simulated under irradiation doses ranging from 0.05 dpa to 0.5 dpa while evaluating the dependencies on temperature and grain size. The interaction between pre-existing defects and irradiation-induced microstructures (point defects, dislocations, clusters, etc.) was visualized and analyzed visually and quantitatively. The results indicate that the irradiation dose increases the number of surviving Frenkel pairs, whereas elevated temperatures reduce their stability. The cluster fraction of interstitials increases with both irradiation dose and temperature, while that of vacancies decreases at higher temperatures due to their lower stability. In the initial phase of the displacement cascade, the density and distribution of dislocations evolve continuously until the annealing stage. The dislocation density at the end of the annealing phase decreases with increasing dose and temperature. The thickness of grain boundaries increases with the irradiation dose, and the regions adjacent to grain boundaries transform into an amorphous state at higher dose levels. As both the irradiation dose and temperature increase, the amorphization process accelerates, and smaller grain size leads to a greater degree of amorphization. Full article

The continuous evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the emergence of variants of concern (VOCs) underscore the critical role of vaccination in pandemic control. These mutations not only enhance viral infectivity but also facilitate immune evasion and diminish vaccine efficacy, necessitating ongoing surveillance and vaccine adaptation. Current SARS-CoV-2 vaccines, including inactivated, live-attenuated, viral vector, protein subunit, virus-like particle, and nucleic acid vaccines, face challenges due to the immune evasion strategies of emerging variants. Moreover, other sarbecoviruses, such as SARS-CoV-1 and SARS-related coronaviruses (SARSr-CoVs) pose a potential risk for future outbreaks. Thus, developing vaccines capable of countering emerging SARS-CoV-2 variants and providing broad protection against multiple sarbecoviruses is imperative. Several innovative vaccine platforms are being investigated to elicit broad-spectrum neutralizing antibody responses, offering protection against both current SARS-CoV-2 variants and other sarbecoviruses. This review presents an updated overview of the key target antigens and therapeutic strategies employed in current SARS-CoV-2 vaccines. Additionally, we summarize ongoing approaches for the development of vaccines targeting infectious sarbecoviruses. Full article

Wheat (Triticum aestivum L.) is the world’s most indispensable staple crop and a vital source of food for human diet. Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), constitutes a severe threat to wheat production and in severe cases, the crop fails completely. Anmai1350 (AM1350) is moderately resistant to leaf rust and powdery mildew, and highly susceptible to sheath blight and fusarium head blight. We found that the length and area of mycelium in AM1350 cells varied at different time points of Pst infection. To investigate the molecular mechanism of AM1350 resistance to Pst, we performed transcriptome sequencing (RNA-seq). In this study, we analyzed the transcriptomic changes of the seedling leaves of AM1350 at different stages of Pst infection at 0 h post-infection (hpi), 6 hpi, 24 hpi, 48 hpi, 72 hpi, and 120 hpi through RNA-seq. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) was used to validate RNA-seq data. It was determined that there were differences in the differentially expressed genes (DEGs) of AM1350, and the upregulation and downregulation of the DEGs changed with the time of infection. At different time points, there were varying degrees of enrichment in the response pathways of AM1350, such as the ”MAPK signaling pathway–plant”, the “plant–pathogen interaction” pathway and other pathways. After Pst infected AM1350, the reactive oxygen species (ROS) content gradually increases. The ROS is toxic to Pst, promotes the synthesis of phytoalexins, and inhibits the spread of Pst. As a result, AM1350 shows resistance to Pst race CYR34. The main objective of this study is to provide a better understanding for resistance mechanisms of wheat in response to Pst infections and to avoid production loss. Full article

Gene expression regulation underpins cellular function and disease progression, yet its complexity and the limitations of conventional detection methods hinder clinical translation. In this review, we define “predict” as the AI-driven inference of gene expression levels and regulatory mechanisms from non-invasive multimodal data (e.g., histopathology images, genomic sequences, and electronic health records) instead of direct molecular assays. We systematically examine and analyze the current approaches for predicting gene expression and diagnosing diseases, highlighting their respective advantages and limitations. Machine learning algorithms and deep learning models excel in extracting meaningful features from diverse biomedical modalities, enabling tools like PathChat and Prov-GigaPath to improve cancer subtyping, therapy response prediction, and biomarker discovery. Despite significant progress, persistent challenges—such as data heterogeneity, noise, and ethical issues including privacy and algorithmic bias—still limit broad clinical adoption. Emerging solutions like cross-modal pretraining frameworks, federated learning, and fairness-aware model design aim to overcome these barriers. Case studies in precision oncology illustrate AI’s ability to decode tumor ecosystems and predict treatment outcomes. By harmonizing multimodal data and advancing ethical AI practices, this field holds immense potential to propel personalized medicine forward, although further innovation is needed to address the issues of scalability, interpretability, and equitable deployment. Full article

Urban green development has become a crucial approach for balancing ecological conservation and socio-economic development. The digital economy (DE) and new-type urbanization (NTU), as technological and social systems, respectively, are both driving urban green development. In this context, furthering their synergistic effects could substantially improve urban sustainability outcomes. Grounded in sociotechnical systems theory, this study applied pooled and multi-period fuzzy-set qualitative comparative analysis (fsQCA) to analyze urban green development pathways in 79 Yellow River Basin cities (2020–2022). The pooled fsQCA indicates that urban green development is driven by synergistic interaction within the NTU-DE subsystem, especially industrial digitalization–spatial urbanization. The multi-period fsQCA further demonstrates that industrial digitization has always existed as a core condition, which means that it plays a more general role. In addition, the Yellow River Basin exhibits distinct regional variations in urban green development, where the downstream region is dominantly driven by DE and spatial urbanization, the upstream region by industrial digitization, and the midstream region demonstrates diversified pathways. This study enhances understanding of complex system interactions in urban green development and provides policy-relevant insights. For policy implementation, local governments should not only prioritize effective synergies between industrial digitization and spatial urbanization but also develop differentiated strategies for the DE and NTU subsystems based on local conditions. Full article