COMPARISON OF BIOACTIVITIES AND COMPOSITION OF CURCUMIN-FREE TURMERIC (\u3cem\u3eCURCUMA LONGA\u3c/em\u3e L.) OILS FROM DIFFERENT SOURCES

Composition, antioxidant capacities and cell inhibition properties of curcumin-free turmeric (Curcuma longa L.) oils from different sources were evaluated by chromatographic method, two different in vitro antioxidative activity assays (DPPH* free radical scavenging assay and reducing power assay) and two different cancer cell lines (Caco-2 and MCF-7). Turmeric oil A (TOA) contains zingiberene, turmerone, and ar-turmerone, while turmeric oil B (TOB) contains 1-phellandrene and α-terpinolene as the major compounds. The antioxidant tests showed that both turmeric oils possessed strong free radical scavenging activities and high reducing powers compared with standard antioxidants such as BHT and commercial rosemary oil (RO). Among the complex constituents in the crude TOA, ar-turmerone, turmerone, curlone and α-terpineol were isolated and found with strong antioxidant activities. The anticancer activity results showed that both turmeric oils possessed high inhibitive capacity against cancer cell lines (ie. Caco-2 and MCF-7) at 20 μL/mL

Fabrication of TiO

The fabrication process and the growth mechanism of titanium/titania nanotubes prepared by anodization process is reviewed, and their applications in the fields of dye sensitized solar cells, photocatalysts, electrochromic devices, gas sensors, and biomaterials are presented. The anodization of Ti thin films on different substrates and the growth process of anodic titanium oxide are described using the current-time curves. Special attention is paid on the influences of the initial film smoothness on the resulted nanoporous morphologies. The “threshold barrier layer thickness model” is used to discuss the growth mechanism. As a case study for gas sensing, anodized highly ordered TiO2 nanotube arrays and nanoporous thin films that show porous surface with an average diameter of 25 nm and interpore distance of 40 nm were prepared. Gas sensors based on such nanotube arrays and nanoporous thin films were fabricated, and their sensing properties were investigated. Excellent H2 gas sensing properties were obtained for sensors prepared from these highly ordered TiO2 nanotube arrays, which present stable response even at a low operating temperature of 90°C. Based on our experimental results, “H-induced O2− desorption” mechanism was used for explaining the hydrogen gas sensing mechanism

CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems

In large-scale recommender systems, retrieving top N relevant candidates accurately with resource constrain is crucial. To evaluate the performance of such retrieval models, Recall@N, the frequency of positive samples being retrieved in the top N ranking, is widely used. However, most of the conventional loss functions for retrieval models such as softmax cross-entropy and pairwise comparison methods do not directly optimize Recall@N. Moreover, those conventional loss functions cannot be customized for the specific retrieval size N required by each application and thus may lead to sub-optimal performance. In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases. Furthermore, we develop the Lambda method, a gradient-based method that invites more flexibility and can further boost the system performance. We evaluate the proposed CROLoss on two public benchmark datasets. The results show that CROLoss achieves SOTA results over conventional loss functions for both datasets with various choices of retrieval size N. CROLoss has been deployed onto our online E-commerce advertising platform, where a fourteen-day online A/B test demonstrated that CROLoss contributes to a significant business revenue growth of 4.75%.Comment: 9 pages, 5 figures. Accepted by by CIKM 202

Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks

Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of striking a delicate balance between achieving precision in learning and ensuring the overall stability of the system. In response to this substantial challenge, this paper introduces a novel DS algorithm rooted in neural network technology. This algorithm not only possesses the capability to extract critical insights from demonstration data but also demonstrates the capacity to learn a candidate Lyapunov energy function that is consistent with the provided data. The model presented in this paper employs a straightforward neural network architecture that excels in fulfilling a dual objective: optimizing accuracy while simultaneously preserving global stability. To comprehensively evaluate the effectiveness of the proposed algorithm, rigorous assessments are conducted using the LASA dataset, further reinforced by empirical validation through a robotic experiment

Evaluation of Urban Infrastructure Investment Efficiency: Empirical Evidence from Heilongjiang Province, China

The rapid growth of urban infrastructure investment in China has brought with it some serious problems that cannot be ignored, such as low investment efficiency and faulty investment decision-making. Therefore, based on the latest research findings related to infrastructure efficiency evaluation theory and evaluation methods, this paper uses empirical evidence from Heilongjiang province to analyze urban infrastructure investment efficiency. To analyze investment efficiency in the province, a new infrastructure investment efficiency evaluation model is developed known as the SDEA-Malmquist model. The model reveals that urban infrastructure investment projects in Heilongjiang province are relatively effective and stable but that the efficiency of such investments varies according to the city in which they are made. Overall efficiency is consistent with the TFC (total final consumption) index, but the index fluctuates within a narrow range between cities due to technological differences

GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation

Recently, graph-based and Transformer-based deep learning networks have demonstrated excellent performances on various point cloud tasks. Most of the existing graph methods are based on static graph, which take a fixed input to establish graph relations. Moreover, many graph methods apply maximization and averaging to aggregate neighboring features, so that only a single neighboring point affects the feature of centroid or different neighboring points have the same influence on the centroid's feature, which ignoring the correlation and difference between points. Most Transformer-based methods extract point cloud features based on global attention and lack the feature learning on local neighbors. To solve the problems of these two types of models, we propose a new feature extraction block named Graph Transformer and construct a 3D point point cloud learning network called GTNet to learn features of point clouds on local and global patterns. Graph Transformer integrates the advantages of graph-based and Transformer-based methods, and consists of Local Transformer and Global Transformer modules. Local Transformer uses a dynamic graph to calculate all neighboring point weights by intra-domain cross-attention with dynamically updated graph relations, so that every neighboring point could affect the features of centroid with different weights; Global Transformer enlarges the receptive field of Local Transformer by a global self-attention. In addition, to avoid the disappearance of the gradient caused by the increasing depth of network, we conduct residual connection for centroid features in GTNet; we also adopt the features of centroid and neighbors to generate the local geometric descriptors in Local Transformer to strengthen the local information learning capability of the model. Finally, we use GTNet for shape classification, part segmentation and semantic segmentation tasks in this paper

Determination of Ice Cloud Models Using MODIS and MISR Data

Representation of ice clouds in radiative transfer simulations is subject to uncertainties associated with the shapes and sizes of ice crystals within cirrus clouds. In this study, we examined several ice cloud models consisting of smooth, roughened, homogeneous and inhomogeneous hexagonal ice crystals with various aspect ratios. The sensitivity of the bulk scattering properties and solar reflectances of cirrus clouds to specific ice cloud models is investigated using the improved geometric optics method (IGOM) and the discrete ordinates radiative transfer (DISORT) model. The ice crystal habit fractions in the ice cloud model may significantly affect the simulations of cloud reflectances. A new algorithm was developed to help determine an appropriate ice cloud model for application to the satellite-based retrieval of ice cloud properties. The ice cloud particle size retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data, collocated with Multi-angle Imaging Spectroradiometer (MISR) observations, is used to infer the optical thicknesses of ice clouds for nine MISR viewing angles. The relative differences between view-dependent cloud optical thickness and the averaged value over the nine MISR viewing angles can vary from 0.5 to 0.5 and are used to evaluate the ice cloud models. In the case for 2 July 2009, the ice cloud model with mixed ice crystal habits is the best fit to the observations (the root mean square (RMS) error of cloud optical thickness reaches 0.365). This ice cloud model also produces consistent cloud property retrievals for the nine MISR viewing configurations within the measurement uncertainties

Advanced progress of the relationship between PCSK9 monoclonal antibodies and hyperglycemic adverse events

Purpose of ReviewLong-term use of statins had been confirmed to cause an increase in hyperglycemic adverse events (HAEs), whose mechanism has been well understood. Proprotein convertase subtilisin/kexin type 9 (PCSK9) monoclonal antibodies (PCSK9-mAbs), a kind of new lipid-lowering drug, can effectively reduce plasma low-density lipoprotein cholesterol levels in patients with CHD and have been widely used. However, animal experiments, Mendelian randomization studies, clinical researches and Meta-analyses which focused on the relationship between PCSK9-mAbs and HAEs had reached different conclusions, which has attracted great attention from clinicians.Recent FindingsThe newest FOURIER-OLE randomized controlled trial followed PCSK9-mAbs users for over 8 years, whose results suggested that long-term use of PCSK9-mAbs did not increase the incidence of HAEs. Newest Meta-analyses also indicated that there was no relationship between PCSK9-mAbs and NOD. Meanwhile, genetic polymorphisms and variants related to PCSK9 might have effects on HAEs.ConclusionAccording to the results of current studies, there is no significant relationship between PCSK9-mAbs and HAEs. However, longer-term follow-up studies are still needed to confirm it. Although PCSK9 genetic polymorphisms and variants may affect the possible occurrence of HAEs, there is no need to perform relevant genetic testing before applying PCSK9-mAbs