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Munkholm Zachariassen posted an update 6 months, 1 week ago
The heat map and fixation trajectory map show convergence, mostly focusing on the position of keywords and key sentences, with relatively large saccade amplitude and more information obtained by one gaze. Moreover, they had a higher skipping reading rate, which enhanced reading speed to obtain information accurately and quickly. These empirical results indicate that mind mapping training was an effective method for improving students’ reading ability.Physicochemical characterization is a crucial step for the successful development of solid dispersions, including the determination of drug crystallinity and molecular interactions. Typically, the detection of molecular interactions will assist in the explanation of different drug performances (e.g., dissolution, solubility, stability) in solid dispersions. Various prominent reviews on solid dispersions have been reported recently. However, there is still no overview of recent techniques for evaluating the molecular interactions that occur within solid dispersions of poorly water-soluble drugs. In this review, we aim to overview common methods that have been used for solid dispersions to identify different bond formations and forces via the determination of interaction energy. In addition, a brief background on the important role of molecular interactions will also be described. The summary and discussion of methods used in the determination of molecular interactions will contribute to further developments in solid dispersions, especially for quick and potent drug delivery applications.Multiple object detection is challenging yet crucial in computer vision. In This study, owing to the negative effect of noise on multiple object detection, two clustering algorithms are used on both underwater sonar images and three-dimensional point cloud LiDAR data to study and improve the performance result. selleck kinase inhibitor The outputs from using deep learning methods on both types of data are treated with K-Means clustering and density-based spatial clustering of applications with noise (DBSCAN) algorithms to remove outliers, detect and cluster meaningful data, and improve the result of multiple object detections. Results indicate the potential application of the proposed method in the fields of object detection, autonomous driving system, and so forth.The accurate and prompt recognition of a driver’s cognitive distraction state is of great significance to intelligent driving systems (IDSs) and human-autonomous collaboration systems (HACSs). Once the driver’s distraction status has been accurately identified, the IDS or HACS can actively intervene or take control of the vehicle, thereby avoiding the safety hazards caused by distracted driving. However, few studies have considered the time-frequency characteristics of the driving behavior and vehicle status during distracted driving for the establishment of a recognition model. This study seeks to exploit a recognition model of cognitive distraction driving according to the time-frequency analysis of the characteristic parameters. Therefore, an on-road experiment was implemented to measure the relative parameters under both normal and distracted driving via a test vehicle equipped with multiple sensors. Wavelet packet analysis was used to extract the time-frequency characteristics, and 21 pivotal features were determined as the input of the training model. Finally, a bidirectional long short-term memory network (Bi-LSTM) combined with an attention mechanism (Atten-BiLSTM) was proposed and trained. The results indicate that, compared with the support vector machine (SVM) model and the long short-term memory network (LSTM) model, the proposed model achieved the highest recognition accuracy (90.64%) for cognitive distraction under the time window setting of 5 s. The determination of time-frequency characteristic parameters and the more accurate recognition of cognitive distraction driving achieved in this work provide a foundation for human-centered intelligent vehicles.Gold nanoparticles (AuNPs) were homogeneously electrodeposited on nitrogen-doped reduced graphene oxide (N-rGO) to modify a glassy carbon electrode (GCE/N-rGO-Au) in order to improve the simultaneous detection of dopamine (DA), ascorbic acid (AA), and uric acid (UA). N-rGO was prepared by the hydrothermal treatment of graphene oxide (GO) and urea at 180 °C for 12 h. AuNPs were subsequently electrodeposited onto the surface of GCE/N-rGO using 1 mM HAuCl4 solution. The morphology and chemical composition of the synthesized materials were characterized by field-emission scanning electron microscopy and X-ray photoelectron spectroscopy. The electrochemical performance of the modified electrodes was investigated through cyclic voltammetry and differential pulse voltammetry measurements. Compared to GCE/rGO-Au, GCE/N-rGO-Au exhibited better electrochemical performance towards the simultaneous detection of the three analytes due to the more homogeneous distribution of the metallic nanoparticles as a result of more efficient anchoring on the N-doped areas of the graphene structure. The GCE/N-rGO-Au-based sensor operated in a wide linear range of DA (3-100 µM), AA (550-1500 µM), and UA (20-1000 µM) concentrations with a detection limit of 2.4, 58, and 8.7 µM, respectively, and exhibited satisfactory peak potential separation values of 0.34 V (AA-DA), 0.20 V, (DA-UA) and 0.54 V (AA-UA). Remarkably, GCE/N-rGO-Au showed a very low detection limit of 385 nM towards DA, not being susceptible to interference, and maintained 90% of its initial electrochemical signal after one month, indicating an excellent long-term stability.Globally increasing environmental awareness and the possibility of increasing price and dwindling supply of traditional petroleum-based plastics have led to a breadth of research currently addressing environmentally friendly bioplastics as an alternative solution. In this context, hemicellulose, as the second richest polysaccharide, has attracted extensive attention due to its combination of such advantages as abundance, biodegradability, and renewability. Herein, in this review, the latest research progress in development of hemicellulose film with regard to application in the field of food packaging is presented with particular emphasis on various physical and chemical modification approaches aimed at performance improvement, primarily for enhancement of mechanical, barrier properties, and hydrophobicity that are essential to food packing materials. The development highlights of hemicellulose film substrate are outlined and research prospects in the field are described.
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