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Abstract Predicting phase stability in high entropy alloys (HEAs), such as phase fractions as functions of composition and temperature, is essential for understanding alloy properties and screening desirable materials. Traditional methods like CALPHAD are computationally intensive for exploring high-dimensional compositional spaces. To address such a challenge, this study explored and compared the effectiveness of random forests (RF) and deep neural networks (DNN) for accelerating materials discovery by building surrogate models of phase stability prediction. For interpolation scenarios (testing on the same order of system as trained), RF models generally produce smaller errors than DNN models. However, for extrapolation scenarios (training on lower-order systems and testing on higher order systems), DNNs generalize more effectively than traditional ML models. DNN demonstrate the potential to predict topologically relevant phase composition when data were missing, making it a powerful predictive tool in materials discovery frameworks. The study uses a CALPHAD dataset of 480 million data points generated from a custom database, available for further model development and benchmarking. Experiments show that DNN models are data-efficient, achieving similar performance with a fraction of the dataset. This work highlights the potential of DNNs in materials discovery, providing a powerful tool for predicting phase stability in HEAs, particularly within the Cr-Hf-Mo-Nb-Ta-Ti-V-W-Zr composition space.

Learning and memory disorder is a cluster of symptoms caused by neuronal aging and other diseases of the central nervous system (CNS). Panax notoginseng saponins (PNS) are a series of saponins derived from the natural active ingredients of traditional Chinese medicine (TCM) that have neuroprotective effects on the central nervous system. In this paper, we review the ameliorative effects and mechanisms of Panax notoginseng saponin-like components on learning and memory disorders to provide valuable references and insights for the development of new drugs for the treatment of learning and memory disorders. Our summary results suggest that Panax ginseng saponins have significant effects on improving learning and memory disorders, and these effects and potential mechanisms are mediated by their anti-inflammatory, anti-apoptotic, antioxidant, β-amyloid lowering, mitochondrial homeostasis in vivo, neuronal structure and function improving, neurogenesis promoting, neurotransmitter release regulating, and probiotic homeostasis in vivo activities. These findings suggest the potential of Panax notoginseng saponin-like constituents as drug candidates for improving learning and memory disorders.

Abstract In recent years, photo‐voltaic (PV) system has become one of the most potential renewable energy power generation technologies because of its many advantages. Considering the influence of the randomness of PV system on the operation and dispatching of power system, the necessity of a comprehensive forecasting model is increased rapidly. This paper proposes a short‐term PV power forecasting method based on MDCM‐GA‐LSTM model to solve the problem of low accuracy of traditional and single forecasting model. First, the meteorological data are pre‐processed based on wavelet denoising algorithm to improve the data quality. Second, a meteorological model is established based on genetic algorithm (GA) to optimize long short‐term memory (LSTM) neural network. Finally, the network is trained with the modified data of the meteorological model as the input and the actual power as the output, and the power forecasting model is established. In this study, the forecasting accuracy is evaluated based on mean absolute error (MAE) and root mean square error (RMSE). The experimental results demonstrated that the MDCM‐GA‐LSTM model outperforms the conventional and single model by a difference of about 98.43% of the MAE and 98.97% of the RMSE error.

Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.

Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.

Abstract Slopes along the highway and railway routes are subjected to not only static loads but also dynamic loads generated by vehicles and trains. The induced excessive deformation potentially poses a threat to slope stability. In terms of the extensive application of ecological slope protection, plants play a critical role in slope stability, as the roots can enhance the shear strength of the soil. This study aims to investigate the influence of different root distribution patterns on the dynamic characteristics induced by cyclic loading. By conducting a group of dynamic triaxial tests, the results indicate that the root system can significantly enhance the liquefaction resistance of the soil when the soil is subjected to lower dynamic loads, and the cross arrangement has a better-reinforced effect than the mixed arrangement. The reinforced effect was not obvious when the soil was subjected to a dynamic load with a larger stress amplitude. In addition, based on the validation of the seed model, a new pore water pressure development model was proposed according to the test results. Overall, the research provides a new model and some innovative observations to better understand the dynamic behavior of root-reinforced soil.

Fruit pedicel (FP) is an important determinant of premium fruit quality that directly affects commercial market value. However, in-depth molecular and genetic basis of pedicel-related traits has not been identified in watermelon. Herein, a quantitative trait locus (QTL) mapping strategy was used to identify the potential genetic regions controlling FP traits based on newly derived whole-genome single nucleotide polymorphism based cleaved amplified polymorphism sequence (SNP-CAPS) markers. Next-generation sequencing based whole-genome re-sequencing of two watermelon parent lines revealed 98.30 and 98.40% of average coverage, 4,989,869 SNP variants, and 182,949 CAPS loci pairs across the reference genome, respectively. A total of 221 sets of codominant markers exhibited 46.42% polymorphism rate and were effectively genotyped within 100-F2:3 derived mapping population. The developed linkage map covered a total of 2,630.49 cM genetic length with averaged 11.90 cM, and depicted a valid marker-trait association. In total, 6 QTLs (qFPL4.1, qFPW4.1, qFPD2.1, qFPD2.2, qFPD8.1, qFPD10.1) were mapped with five major effects and one minor effect between the whole genome adjacent markers positioned over distinct chromosomes (02, 04, 08, 10), based on the ICIM-ADD mapping approach. These significant QTLs were similarly mapped in delimited flanking regions of 675.10, 751.38, 859.24, 948.39, and 947.51 kb, which collectively explained 8.64–13.60% PVE, respectively. A highly significant and positive correlation was found among the observed variables. To our knowledge, we first time reported the mapped QTLs/genes affecting FP traits of watermelon, and our illustrated outcomes will deliver the potential insights for fine genetic mapping as well as functional gene analysis through MAS-based breeding approaches.

The melon fruit surface groove (fsg) not only affects peel structure and causes stress-induced fruit cracking but also fits consumers’ requirements in different regions. In this study, genetic inheritance analysis of three F2 populations derived from six parental lines revealed that the fsg trait is controlled by a simple recessive inherited gene. Through bulked segregant analysis sequencing (BSA-seq), the Cmfsg locus was detected in an 8.96 Mb interval on chromosome 11 and then initially mapped to a region of approximately 1.15 Mb. Further fine mapping with a large F2 population including 1,200 plants narrowed this region to 207 kb containing 11 genes. A genome-wide association study (GWAS) with 187 melon accessions also produced the same chromosome region for the Cmfsg locus. Due to the rare molecular markers and lack of mutations in the coding and promoter regions of the 11 candidate genes in the fine-mapped interval, we conducted in silico BSA to explore the natural melon panel to predict candidate genes for the Cmfsg locus. A 1.07 kb segment upstream of MELO3C019694.2 (annotated as the AGAMOUS MADS-box transcription factor) exhibited a correlation with the grooved and non-grooved accessions among the F2 individuals, and a natural panel consisted of 17 melon accessions. The expression level of MELO3C019694.2 in the pericarp was higher in grooved lines than in non-grooved lines and was specifically expressed in fruit compared with other tissues (female flower, male flower, root, and leaf). This work provides fundamental information for further research on melon fsg trait formation and molecular markers for melon breeding.

Abstract A facile way was developed to fabricate yolk-shell composites with tunable Cu cores encapsulated within hollow carbon spheres (Cu@C) with an average diameter about 210 nm and cavity size about 80 nm. During pyrolysis, the confined nanospace of hollow cavity ensures that the nucleation-and-growth process of Cu nanocrystals take place exclusively inside the cavities. The size of Cu cores can be easily tuned from 30 to 55 nm by varying the copper salt concentration. By deliberately creating shell porosity through KOH chemical activation, at an optimized KOH/HCS mass ratio of 1/4, the catalytic performance for the oxidative carbonylation of methanol to dimethyl carbonate (DMC) of the activated sample is enhanced remarkably with TOF up to 8.6 h−1 at methanol conversion of 17.1%. The activated yolk-shell catalyst shows promising catalytic properties involving the reusability with slight loss of catalytic activity and negligible leaching of activated components even after seven recycles, which is beneficial to the implementation of clean production for the eco-friendly chemical DMC thoroughly.

In this study, a facile and effective method is adopted to prepare mechanochemically robust super antifouling membrane surfaces. During the process, vinyl trimethoxy silane (VTMS) was used as the reactive intermediate for coupling the hydrophilic inorganic SiO2 nanoparticle layer on to the organic ultra-high-molecular-weight polyethylene (UHMWPE) membrane surface, which created hierarchical nanostructures and lower surface energy simultaneously. The physical and chemical properties of the modified UHMWPE composite membrane surface were investigated. FTIR and XPS showed the successful chemical grafting of VTMS and SiO2 immobilization, and this modification could effectively enhance the membrane’s surface hydrophilicity and filtration property with obviously decreased surface contact angle, the pure water flux and bovine serum albumin (BSA) rejection were 805 L·m−2·h−1 and 93%, respectively. The construction of the hydrophilic nano-SiO2 layer on the composite membrane surface for the improvement of membrane antifouling performance was universal, water flux recovery ratio values of BSA, humic acid (HA), and sodium alginate (SA) were all up to 90%. The aim of this paper is to provide an effective approach for the enhancement of membrane antifouling performance by the construction of a hydrophilic inorganic layer on an organic membrane surface.

Based on the airflow rotation concept, a new packaging structure was designed to meet the performance requirements of China-VI stage emission regulations. The new structure is a hybrid structure of spiral space with small blades and conical plates. The Computational Fluid Dynamics (CFD) simulation was used to analyze the velocity uniformity and ammonia uniformity of the flow field, and the results showed that velocity uniformity and ammonia uniformity were higher than 0.95, and the possibility of crystallization risk was very low. The actual performance of the new structural prototype was verified by means of engine bench testing. The test results were excellent and the conversion efficiency reached 0.96. The new selective catalytic reduction (SCR) urea mixed structure design greatly improved the conversion efficiency of the after-treatment system and has excellent anti-crystallization performance.

Parasitism genes encoding secreted effector proteins of plant-parasitic nematodes play important roles in facilitating parasitism. An annexin-like gene was isolated from the cereal cyst nematode Heterodera avenae (termed Ha-annexin) and had high similarity to annexin 2, which encodes a secreted protein of Globodera pallida. Ha-annexin encodes a predicted 326 amino acid protein containing four conserved annexin domains. Southern blotting revealed that there are at least two homologies in the H. avenae genome. Ha-annexin transcripts were expressed within the subventral gland cells of the pre-parasitic second-stage juveniles by in situ hybridization. Additionally, expression of these transcripts were relatively higher in the parasitic second-stage juveniles by quantitative real-time RT-PCR analysis, coinciding with the time when feeding cell formation is initiated. Knockdown of Ha-annexin by method of barley stripe mosaic virus-based host-induced gene silencing (BSMV-HIGS) caused impaired nematode infections at 7 dpi and reduced females at 40 dpi, indicating important roles of the gene in parasitism at least in early stage in vivo. Transiently expression of Ha-ANNEXIN in onion epidermal cells and Nicotiana benthamiana leaf cells showed the whole cell-localization. Using transient expression assays in N. benthamiana, we found that Ha-ANNEXIN could suppress programmed cell death triggered by the pro-apoptotic mouse protein BAX and the induction of marker genes of PAMP-triggered immunity (PTI) in N. benthamiana. In addition, Ha-ANNEXIN targeted a point in the mitogen-activated protein kinase (MAPK) signaling pathway downstream of two kinases MKK1 and NPK1 in N. benthamiana.

Tensile instability is one of the major obstacles to particle methods in fluid simulation, which would cause particles to clump in pairs under tension and prevent fluid simulation to generate small-scale thin features. To address this issue, previous particle methods either use a background pressure or a finite difference scheme to alleviate the particle clustering artifacts, yet still fail to produce small-scale thin features in free-surface flows. In this article, we propose a dual-particle approach for simulating incompressible fluids. Our approach involves incorporating supplementary virtual particles designed to capture and store particle pressures. These pressure samples undergo systematic redistribution at each time step, grounded in the initial positions of the fluid particles. By doing so, we effectively reduce tensile instability in standard SPH by narrowing down the unstable regions for particles experiencing tensile stress. As a result, we can accurately simulate free-surface flows with rich small-scale thin features, such as droplets, streamlines, and sheets, as demonstrated by experimental results. [ABSTRACT FROM AUTHOR]