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The preparation of methanol (CH 3 OH) from methane (CH 4) oxidation has been viewed as a promising technique to obtain liquid fuel from natural gas. It was demonstrated that oxygen was adsorbed on single palladium (PdO) as an excellent catalyst for the CH 4 -to-CH 3 OH conversion under the scheme of density functional theory. The catalyst PdO loaded on boron sheet shows excellent stability and CH 3 OH selectivity. Computational calculations reveal that the boron substrate remarkably tunes the state of palladium. Image 1 • Boron nanosheet has been explored as the substrate to host PdO catalyst. • DFT calculations demonstrate that PdO-B is a prosing catalysts for CH 4 conversion. • Boron can effectively mediate PdO to achieve high stability and selectivity. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the efficacy of deep learning architectures in enhancing the interpretation of skin images, thereby aiding in the classification and segmentation of skin cancer. However, the existing deep learning techniques predominantly focus on either segmentation or classification and are designed for normally distributed data. Addressing this limitation, the present study introduces a hybrid ensemble deep learning approach for skin cancer classification and segmentation. This proposed model amalgamates Residual Learning Machines, Swin Transformers, and Fast Neural Networks (FNN) to proficiently manage diverse sets of non-uniformly distributed data, thereby augmenting diagnostic accuracy. The efficacy of this approach was evaluated using various skin cancer datasets, including ISIC-2008, PH-2, and HM007. The assessment involved several performance metrics, such as the Matthew correlation coefficient (MCC), recall, F1-score, specificity, accuracy, and precision. Moreover, to underscore the superiority of the proposed model, its performance was juxtaposed with that of previous efforts. Results from repeated trials indicate that the proposed model achieved 98.78% classification accuracy, 98.7% precision, 98.7% F1-score, 98.64% average recall, and an MCC of 0.9863 across different skin disease datasets. Demonstrating consistent superiority over existing methods, the proposed approach shows considerable potential in revolutionizing the diagnostics of skin cancer. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the efficacy of deep learning architectures in enhancing the interpretation of skin images, thereby aiding in the classification and segmentation of skin cancer. However, the existing deep learning techniques predominantly focus on either segmentation or classification and are designed for normally distributed data. Addressing this limitation, the present study introduces a hybrid ensemble deep learning approach for skin cancer classification and segmentation. This proposed model amalgamates Residual Learning Machines, Swin Transformers, and Fast Neural Networks (FNN) to proficiently manage diverse sets of non-uniformly distributed data, thereby augmenting diagnostic accuracy. The efficacy of this approach was evaluated using various skin cancer datasets, including ISIC-2008, PH-2, and HM007. The assessment involved several performance metrics, such as the Matthew correlation coefficient (MCC), recall, F1-score, specificity, accuracy, and precision. Moreover, to underscore the superiority of the proposed model, its performance was juxtaposed with that of previous efforts. Results from repeated trials indicate that the proposed model achieved 98.78% classification accuracy, 98.7% precision, 98.7% F1-score, 98.64% average recall, and an MCC of 0.9863 across different skin disease datasets. Demonstrating consistent superiority over existing methods, the proposed approach shows considerable potential in revolutionizing the diagnostics of skin cancer. [ABSTRACT FROM AUTHOR]

A latent factor analysis (LFA)-based model has outstanding performance in extracting desired patterns from High-dimensional and Sparse (HiDS) data for building a recommender systems. However, they mostly fail in acquiring non-linear features from an HiDS matrix. An AutoEncoder (AE)-based model can address this issue efficiently, but it requires filling unknown data of an HiDS matrix with pre-assumptions that leads to the following limitations: a) prefilling unknown data of an HiDS matrix might skew its known data distribution to generate ridiculous recommendations; and b) incorporating a deep AE-style structure to improve its representative learning ability. Experimental results on three HiDS matrices from real recommender systems show that an FDAE-based model significantly outperforms state-of-the-art recommenders in terms of recommendation accuracy. Meanwhile, its computational efficiency is comparable with the most efficient recommenders with the help of parallelization. [ABSTRACT FROM AUTHOR]

Genome wide association studies (GWAS) have discovered a few of single nucleotide polymorphisms (SNPs) related to major psychiatric disorders. However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five psychiatric disorders using multivariate adaptive association tests. Summary statistics of five psychiatric disorders were downloaded from Psychiatric Genomics Consortium. We applied linkage disequilibrium score regression (LDSC) to estimate genetic correlation and conducted tissue and cell type specificity analyses based on Multi-marker Analysis of GenoMic Annotation (MAGMA). Then, we identified the pleiotropic genes using MTaSPUsSet and aSPUs tests. We ultimately performed the functional analysis for pleiotropic genes. We confirmed the significant genetic correlation and brain tissue and neuron specificity among five disorders. 100 pleiotropic genes were detected to be significantly associated with five psychiatric disorders, of which 55 were novel genes. These genes were functionally enriched in neuron differentiation and synaptic transmission. The effect direction of pleiotropic genes couldn't be distinguished due to without individual-level data. We identified pleiotropic genes using multivariate adaptive association tests and explored their biological function. The findings may provide novel insight into the development and implementation of prevention and treatment as well as targeted drug discovery in practice. • The MTaSPUsSet, a novel adaptive association test, considers association patterns. • More pleiotropic genes were found by aggregating multiple related phenotypes. • It is a cost-effective study based on GWAS summary statistics. [ABSTRACT FROM AUTHOR]