A Location Prediction Algorithm with Daily Routines in Location-Based Participatory Sensing Systems

Mobile node location predication is critical to efficient data acquisition and message forwarding in participatory sensing systems. This paper proposes a social-relationship-based mobile node location prediction algorithm using daily routines (SMLPR). The SMLPR algorithm models application scenarios based on geographic locations and extracts social relationships of mobile nodes from nodes' mobility. After considering the dynamism of users' behavior resulting from their daily routines, the SMLPR algorithm preliminarily predicts node's mobility based on the hidden Markov model in different daily periods of time and then amends the prediction results using location information of other nodes which have strong relationship with the node. Finally, the UCSD WTD dataset are exploited for simulations. Simulation results show that SMLPR acquires higher prediction accuracy than proposals based on the Markov model

Study on the Drug Targets and Molecular Mechanisms of Rhizoma Curcumae in the Treatment of Nasopharyngeal Carcinoma Based on Network Pharmacology

Aim. To analyse the target of Rhizoma Curcumae in nasopharyngeal carcinoma by using network pharmacological techniques and to explore the associated molecular mechanism. Methods. The targets of nasopharyngeal carcinoma were retrieved from the GeneCards database. At the same time, the drug therapeutic targets of Rhizoma Curcumae were obtained from the TCMSP and SymMap databases. The data were imported into the STRING database and Cytoscape 3.7.1 to construct a network of “Chinese medicine component-target-disease” interactions; then, the intersection was screened as the core Rhizoma Curcumae antinasopharyngeal cancer targets. Through GO target function and KEGG pathway enrichment analyses of the core targets, we predicted the biological processes and key signalling pathways involved in the Rhizoma Curcumae treatment of nasopharyngeal carcinoma. Results. Twenty-five core targets of Rhizoma Curcumae in nasopharyngeal carcinoma were mined: TP53, BCL2 ICAM1 RXRA, TLR3 and TLR9, TNF, PTGS2, IL-6, CTSD, MMP2, MMP9, MMP14, TIMP2, ABCC1, ABCB1, ABCG2, and so on. The results of visual analysis showed that the Rhizoma Curcumae treatment of nasopharyngeal carcinoma mainly involves leukocyte adhesion to vascular endothelial cells, positive regulation of NF-κB import into the nucleus, regulation of the reactive oxygen species biosynthetic and metabolic process, regulation of the chemokine biosynthetic and metabolic process, various cancer-related signalling pathways, and a variety of cytokine signal transduction pathways, such as the NF-κB, TLR, IL-17, and TNF signalling pathways. Conclusion. The core targets predicted by our research can be used as molecular markers for the treatment and prediction of nasopharyngeal carcinoma. The mechanism of Rhizoma Curcumae treatment in NPC may be related to immune regulatory pathways, the inhibition of cancer cell proliferation, metastasis, and angiogenesis, as well as the regulation of tumour microenvironment. Combined with the prediction of its associated mechanism of action, the core targets can provide targeted reference value for subsequent drug development related to Curcuma

Effect of Dodecane-Oleic Acid Collector Mixture on the Evolution of Wetting Film between Air Bubble and Low-Rank Coal

The wetting film evolution process is essential for flotation, especially in bubble–particle attachment. A mixed collector has been proved effective in promoting flotation. In this paper, the effect of a mixed collector (MC) composed by n-dodecane (D) and oleic acid (OA) on wetting film evolution was investigated using the extended Derjagin–Landau–Verwey–Overbeek (EDLVO) theory, the Stefan–Reynolds model, induction time, and zeta potential measurement. The hydrophobic force constant between bubble and coal treated by different collectors was analyzed. The results showed that MC was superior in reducing the induction time and increasing the zeta potential. When bubbles interacted with coal treated by MC, they had relatively low interaction energy, high critical film thickness, and high drainage rate. The order of hydrophobic force constant was no reagent < D < OA < MC. It indicated that the hydrophobic interaction between bubbles and coal particles treated by MC was the strongest because of the synergistic effect of D and OA

Hierarchical Detection of <i>Gastrodia elata</i> Based on Improved YOLOX

Identifying the grade of Gastrodia elata in the market has low efficiency and accuracy. To address this issue, an I-YOLOX object detection algorithm based on deep learning and computer vision is proposed in this paper. First, six types of Gastrodia elata images of different grades in the Gastrodia elata planting cooperative were collected for image enhancement and labeling as the model training dataset. Second, to improve feature information extraction, an ECA attention mechanism module was inserted between the backbone network CSPDarknet and the neck enhancement feature extraction network FPN in the YOLOX model. Then, the impact of the attention mechanism and application position on model improvement was investigated. Third, the 3 × 3 convolution in the neck enhancement feature extraction network FPN and the head network was replaced by depthwise separable convolution (DS Conv) to reduce the model size and computation amount. Finally, the EIoU loss function was used to predict boundary frame regression at the output prediction end to improve the convergence speed of the model. The experimental results indicated that compared with the original YOLOX model, the mean average precision of the improved I-YOLOX network model was increased by 4.86% (97.83%), the model computation was reduced by 5.422 M (reaching 3.518 M), the model size was reduced by 20.6 MB (reaching 13.7 MB), and the image frames detected per second increased by 3 (reaching 69). Compared with other target detection algorithms, the improved model outperformed Faster R-CNN, SSD-VGG, YOLOv3s, YOLOv4s, YOLOv5s, and YOLOv7 algorithms in terms of mean average precision, model size, computation amount, and frames per second. The lightweight model improved the detection accuracy and speed of different grades of Gastrodia elata and provided a theoretical basis for the development of online identification systems of different grades of Gastrodia elata in practical production

Effect of Dodecane-Oleic Acid Collector Mixture on the Evolution of Wetting Film between Air Bubble and Low-Rank Coal

The wetting film evolution process is essential for flotation, especially in bubble–particle attachment. A mixed collector has been proved effective in promoting flotation. In this paper, the effect of a mixed collector (MC) composed by n-dodecane (D) and oleic acid (OA) on wetting film evolution was investigated using the extended Derjagin–Landau–Verwey–Overbeek (EDLVO) theory, the Stefan–Reynolds model, induction time, and zeta potential measurement. The hydrophobic force constant between bubble and coal treated by different collectors was analyzed. The results showed that MC was superior in reducing the induction time and increasing the zeta potential. When bubbles interacted with coal treated by MC, they had relatively low interaction energy, high critical film thickness, and high drainage rate. The order of hydrophobic force constant was no reagent < D < OA < MC. It indicated that the hydrophobic interaction between bubbles and coal particles treated by MC was the strongest because of the synergistic effect of D and OA

How to Construct a Power Knowledge Graph with Dispatching Data?

Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of power dispatching behaviors by semi-automated labeling. And then, we propose a model, called the BiLSTM-CRF model, to extract entities and identify the dispatching behavior relationship patterns. Finally, we construct a knowledge graph of power dispatching data. The knowledge graph provides an underlying knowledge model for automated power dispatching and related services and helps dispatchers perform better power dispatch knowledge retrieval and other operations during the dispatch process

PU.1-Silenced Dendritic Cells Induce Mixed Chimerism and Alleviate Intestinal Transplant Rejection in Rats via a Th1 to Th2 Shift

Background/Aims: Intestinal transplantation is an effective treatment for end-stage bowel failure; however, graft rejection and the toxicity associated with non-specific immunosuppression are major limitations of this procedure. Studies have shown that mixed chimerism can produce post-transplantation immune tolerance. Here, we demonstrate that in rat intestinal transplantation, PU.1-silenced dendritic cells (DCs) plus bone marrow (BM) cell transfusion results in mixed chimerism, and we investigate the mechanisms responsible for the effects of mixed chimerism rejection. Methods: In a model of intestinal transplantation, male Brown Norway rats were the donors, and female Lewis rats were the recipients that were randomly divided into 4 groups: control, BM, BM-imDCs and BM-PU.1. The dynamic changes in graft morphology, rejection scoring and serum concentrations of Th1/Th2-related cytokines were investigated on postoperative days 0, 7, 14, 21, and 30. Results: The BM-PU.1 group had better graft health, milder pathologic injuries, and lower rejection grades compared with the other groups. The rates of mixed chimerism were significantly highest in the BM-PU.1 group and correlated with decreases in serum IL-2 and increases in serum IL-10. Conclusion: Transfusion of PU.1-silenced DCs and BM cells induces stable mixed chimerism and has the potential to reduce pathologic injuries via a pro-Th2 shift in the Th1/Th2 balance

3D Printing of Artificial Leaf with Tunable Hierarchical Porosity for CO₂ Photoreduction

The development of new pathways for 3D artificial photosynthetic systems (APS) with controllable architectures and tunable hierarchical porosity on a large scale is significant. Herein, we demonstrate a 3D printing approach for fabricating artificial microleaves with 3D architectures spanning orders of magnitude from nanometers to centimeters in a rapid, programmable, and scalable manner. TiO₂-based inks served as a preliminary prototype, with surfactants and silica nanospheres incorporated for porosity modification. Thus, a TiO₂-based ink is developed to allow for the fabrication of porosity-tunable hierarchical 3D architectures with high surface area (up to ∼259 m²g^–1) and structural integrity with well-designed patterns. The artificial microleaves have macropore architectures comparable to those of natural leaves, indicating their efficient mass transfer ability. Artificial photosynthesis via CO₂ reduction enhances CO and CH₄ evolution on the 3D printed APS by up to 2-fold and 6-fold, respectively, compared with the levels observed for the corresponding powder counterparts. Furthermore, gas diffusion behaviors, closely related to the gas-phase reaction, are investigated by theoretical simulation to reveal the hierarchical structural effects on catalytic efficiency. The strategy is proven to be critical and demonstrates obvious advantages in the potential scale-up of 3D APS device manufacturing