Search

Brain imaging genetics studies the genetic basis of brain structures and functions via integrating both genotypic data such as single nucleotide polymorphism (SNP) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few of QTs and are not designed for feature selection from a large number of QTs; while existing SCCA methods typically employ only one modality of QTs to study its association with SNPs. Both MTL and SCCA encounter computational challenges as the number of SNPs increases. In this paper, combining the merits of MTL and SCCA, we propose a novel multi-task SCCA (MTSCCA) learning framework to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. Using the G 2,1-norm regularization, MTSCCA treats all SNPs in the same group together to enforce sparsity at the group level. The l 2 , 1 -norm penalty is used to jointly select features across multiple tasks for SNPs, and across multiple modalities for QTs. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains improved performance regarding both correlation coefficients and canonical weights patterns. In addition, our method runs very fast and is easy-to-implement, and thus could provide a powerful tool for genome-wide brain-wide imaging genetic studies.

This study aims at counteracting the problem of rejuvenated asphalt with poor performance and weak secondary anti-aging ability by improving the existing biomass rejuvenator. In this study, a carbon nanomaterial, graphene oxide (GO) with excellent anti-aging performance, was introduced to the wood tar-based rejuvenator (WR) to prepare a composite rejuvenator. Based on laboratory tests, the effects of the GO–wood tar-based composite rejuvenator (GWCR) on the performance of aged asphalt and on the secondary aging performance were investigated, and its rejuvenation mechanism was analyzed. The results indicate that the GWCR can increase the penetration, ductility, and creep rate (m) of aged asphalt while decreasing its softening point, rutting factor (G*/sinδ), and stiffness modulus (S). This indicates that the high-temperature resistance to the permanent deformation ability of aged asphalt degrades, while the low-temperature performance improves, and all values are slightly greater than those of the 70# original base asphalt. After PAV aging, the S value of the GO–wood tar-based composite rejuvenated asphalt (GWCRA) increased by 83.71%, while the m value decreased by 49.45%. The secondary aging resistance of the GWCRA is better than that of 70# original base asphalt, RA-75 rejuvenated asphalt, and wood tar-based rejuvenated asphalt. When adding the GWCR into aged asphalt, the content of saturates and aromatics in the asphalt increases by 1.08% and 11.1%, respectively. In contrast, the content of asphaltenes and resins decreases by 6.288% and 5.9%, respectively. As a result, the colloidal structure of the aged asphalt transfers from a gel to a sol–gel state. The surface roughness of the GWCRA increases by the synergistic effect of GO and wood tar, making its adhesion better than that of the 70# original base asphalt. Adding GO can improve the performance of wood tar rejuvenated asphalt (WRA) with high-temperature deformation resistance and resistance to secondary aging, and effectively make up for the defects in the performance of WRA rejuvenated asphalt, so as to extend the service life of asphalt pavements, thus increasing the value of wood tar engineering applications, which is of great practical significance. [ABSTRACT FROM AUTHOR]

Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose [Formula: see text]-norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the [Formula: see text]-norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce the estimation bias in regression tasks. But using them in SCCA remains largely unexplored. In this paper, we design a unified non-convex SCCA model, based on seven non-convex functions, for unbiased estimation and stable feature selection simultaneously. We also propose an efficient optimization algorithm. The proposed method obtains both higher correlation coefficients and better canonical loading patterns. Specifically, these SCCA methods with non-convex penalties discover a strong association between the APOE e4 rs429358 SNP and the hippocampus region of the brain. They both are Alzheimer's disease related biomarkers, indicating the potential and power of the non-convex methods in brain imaging genetics.

This study utilized molecular dynamics simulation to investigate the adhesion process between wood tar-rejuvenated asphalt and acid/alkaline aggregate. Initially, various indicators including the contact area, cohesion coefficient, and interaction energy were employed to assess the adhesion effect under dry conditions. This revealed the action mechanism of the wood tar-rejuvenator in enhancing the adhesion performance between aged asphalt and aggregate. Subsequently, an asphalt–water–aggregate interface model was developed to simulate the water damage process of the asphalt mixture. This aimed to unveil the damage mechanism of water intrusion on the adhesion performance of the asphalt–aggregate interface and evaluate the water damage resistance of wood tar-rejuvenated asphalt through adhesion energy, stripping work, and the energy ratio. The findings indicate that wood tar-rejuvenated asphalt exhibits favorable adhesion properties with both acid and alkaline aggregates. The addition of wood tar-rejuvenated asphalt increased the interaction energy between aged asphalt and acid and alkali aggregates by 67.75 kJ/mol and 97.3 kJ/mol, respectively. The addition of a wood tar rejuvenator enhances the interaction energy between aged asphalt and aggregate, thereby increasing mutual attraction and enlarging the contact area. The adhesion between asphalt and aggregates hinges on the interaction between asphaltene and aggregates, and the wood tar rejuvenator reduces the diffusion ability of asphaltene in the attractive state of the aggregate, resulting in stable aggregation. Moisture intrusion increased the aggregation distance between asphaltene and aggregate by 14.1% and decreased the degree of aggregation by 24.0%, thereby reducing the interaction energy. The extent of damage caused by water intrusion is linked to the aggregation distance, with greater distances leading to deeper damage. Under wet conditions, the interaction energy of wood tar-rejuvenated asphalt increased by 78.2% in the acidic aggregate system and 98.1% in the basic aggregate system compared with aged asphalt. Meanwhile, wood tar-based rejuvenated asphalt improves the adhesion between aged asphalt and aggregate and reduces the stripping function of asphalt affected by water replacement, which results in the ER value of wood tar-rejuvenated asphalt being higher than that of the original asphalt by 0.12 and 0.22 in the acidic and alkaline environments, respectively, thus showing excellent resistance to water damage. This study provides new criteria for the selection of rejuvenators for waste asphalt, which will help in the future selection of superior rejuvenators for aged asphalt and reduce the possibility of choosing the wrong rejuvenator. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, the manufacturing sector is moving from automation towards intelligence. The application of new generation information and communication technologies (ICTs) improves the interconnection and transparency of intelligent manufacturing (IM) systems, which will change how information interacts and work is done, thus changing how work should be managed. These changes require the following characteristics for IM production and operations management (POM): integration, flexibility and networking, autonomous and collaborative decision-making, learning-based operations management, self-optimisation and adaptability, and proactive decision-making. This paper presents the state of the art, current challenges, and future directions of IM-related POM research from the perspectives of these characteristics through a systematic literature review. Descriptive and thematic analyses of 208 research articles published between 2005 and 2020 are provided. The review and discussions focus on five research themes, i.e. value creation mechanisms, resource configuration and capacity planning, production planning, scheduling, and logistics. [ABSTRACT FROM AUTHOR]