Background: The Food and Agriculture Organization has reported approximately 40 % of food loss due to damage from plant pests and diseases, including fungal infections. Continuous application of agrichemicals for cont...
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Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover f...
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We highlight with first-principles molecular dynamics the persistence of intrinsic 〈111〉 Ti off-centerings for BaTiO3 in its cubic paraelectric phase. Intriguingly, these are inconsistent with the Pm3¯m space gro...
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We highlight with first-principles molecular dynamics the persistence of intrinsic 〈111〉 Ti off-centerings for BaTiO3 in its cubic paraelectric phase. Intriguingly, these are inconsistent with the Pm3¯m space group often used to atomistically model this phase using density-functional theory or similar methods. Therefore, we deploy a systematic symmetry analysis to construct representative structural models in the form of supercells that satisfy a desired point symmetry but are built from the combination of lower-symmetry primitive cells. We define as structural prototypes the smallest of these that are both energetically and dynamically stable. Remarkably, two 40-atom prototypes can be identified for paraelectric BaTiO3; these are also common to many other ABO3 perovskites. These prototypes can offer structural models of paraelectric phases that can be used for the computational engineering of functional materials. Last, we show that the emergence of B-cation off-centerings and the primitive-cell phonon instabilities is controlled by the equilibrium volume, in turn, dictated by the filler A cation.
In this study,Plasma Electrolytic Oxidation(PEO)coatings with Carbon Nanotubes(CNTs)were prepared on Selective Laser Melting(SLM)AlSi10Mg alloy by PEO treatment with CNTs addition to the *** mechanism of doping CNTs i...
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Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast...
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Background The removal of hazardous dyes from water by magnetic adsorbents has received significant interest due to their low cost and ease of separation from the solution phase. This study explores the efficacy of a ...
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Background: LZ91 Magnesium alloy with a dual-phase structure has improved mechanical properties and a low density of about 1.48 g/cm3 but their limited resistance to corrosion limits their application. Therefore, it i...
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The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Tra...
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The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with machine learning. ATR-FTIR offers non-destructive and non-invasive advantages, requiring minimal sample preparation. By detecting specific chemical bonds in blood components, it enables the differentiation of various body fluids. However, the subjective interpretation of the spectra poses challenges in distinguishing different fluids. To address this, we employ machine learning techniques. Machine learning is extensively used in chemometrics to analyze chemical data, build models, and extract useful information. This includes both unsupervised learning and supervised learning methods, which provide objective characterization and differentiation. The focus of this study was to identify human and porcine blood on substrates using ATR-FTIR spectroscopy. The substrates included paper, plastic, cloth, and wood. Data preprocessing was performed using Principal Component Analysis (PCA) to reduce dimensionality and analyze latent variables. Subsequently, six machine learning algorithms were used to build classification models and compare their performance. These algorithms comprise Partial Least Squares Discriminant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performance of different identification models still varies based on the type of substrate. The integration of these disciplines enables researchers to harness the power of data-driven approaches for solving complex forensic problems. Th
Ions released from inorganic biomaterials play a vital role in defining cell identity. However, the effect of inorganic ions on cellular functions has yet to be investigated at the transcriptomic level, representing a...
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Global environmental challenges and energy crises have driven researchers to develop multifunctional and highly efficient nanomaterials. This study presents dual-functional NiMoO4 (NMO)/NiO hierarchical microspheres t...
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