This study presents a novel approach to indoor positioning leveraging radio frequency identification (RFID) technology based on received signal strength indication (RSSI). The proposed methodology ...integrates Gaussian Kalman filtering for effective signal preprocessing and a time-distributed auto encoder-gated recurrent unit (TAE-GRU) model for precise location prediction. Addressing the prevalent challenges of low accuracy and extended localization times in current systems, the proposed method significantly enhances the preprocessing of RSSI data and effectively captures the temporal relationships inherent in the data. Experimental validation demonstrates that the proposed approach achieves a 75.9% improvement in localization accuracy over simple neural network methods and markedly enhances the speed of localization, thereby proving its practical applicability in real-world indoor localization scenarios.
Three-dimensional (3D) geological models are typical data sources in 3D mineral prospectivity modeling. However, identifying prospectivity-informative predictor variables from 3D geological models is ...a challenging and work-intensive task. Motivated by the ability of convolutional neural networks (CNNs) to learn intrinsic features, in this paper, we present a novel method that leverages CNNs to learn 3D mineral prospectivity from 3D geological models. By exploiting this learning ability, the proposed method simplifies the complex correlations of mineralization and circumvent the need for designing the predictor variables. Specifically, to analyze unstructured 3D geological models using CNNs—whose inputs should be structured—we develop a 2D CNN framework where the geometry of geological boundary is compiled and reorganized into multi-channel images and fed into the CNN. This ensures the effective and efficient training of the CNN while facilitating the representation of mineralization control. The presented method is applied to a typical structure-controlled hydrothermal deposit, the Dayingezhuang gold deposit in eastern China; the presented method is compared with prospectivity modeling methods using designed predictor variables. The results show that the presented method has a performance boost in terms of the 3D prospectivity modeling and decreases the workload and prospecting risk in the prediction of deep-seated orebodies.
•A 3D mineral prospectivity modeling method based on convolutional neural networks.•Deep learning of 3D mineral prospectivity from the 3D geological models.•Reorganizing the unstructured 3D geological models into convolutional neural networks.•A 3D prospectivity modeling framework free of hand-designed predictor variables.
Background/Aims: Ginsenoside Rg1 has been demonstrated to exhibit neuroprotective effects in various studies. This study aimed to investigate the neuronal mechanisms underlying the neuroprotective ...and antidepressant-like effects of ginsenoside Rg1 in a rat model of depression. Methods: Chronic unpredictable mild stress was used to induce depression-like behaviors in rats. Transmission electron microscopy was used to observe neuronal synapses within the basolateral amygdala (BLA). The expression of microRNA (miR)-134 in the BLA was verified by real-time quantitative PCR. Finally, the synaptic plasticity-associated proteins CAMP-response element binding protein (CREB) and brain-derived neurotrophic factor (BDNF) were detected by immunoblotting. Results: Results showed that chronic stress effectively induced depression-like behaviors in rats, which were associated with significant ultrastructural changes within BLA neurons. Moreover, chronic stress decreased the expression of miR-134 in the BLA, which was accompanied by decreased phosphorylation of CREB and decreased expression of BDNF. Remarkably, chronic administration of ginsenoside Rg1 (40 mg/kg, i.p., 5 weeks) significantly ameliorated the neuronal structural abnormalities and biochemical changes induced by chronic stress, as well as preventing depression-like behaviors in these rats. Conclusion: Results suggested that ginsenoside Rg1 may exhibit neuroprotection and antidepressant-like effects by activating the CREB-BDNF system within the BLA in this rat model of depression. Amelioration of depression-like behaviors by ginsenoside Rg1 appears to involve modulation of the synapse-associated factor miR-134 within the BLA. Therefore, these findings demonstrate some of the neuronal mechanisms associated with depression and the therapeutic potential of ginsenoside Rg1 for use in the treatment of depression in clinical trials.
Functional carbogenic quantum dots (C-dots) were synthesized by a pyrolysis approach with strong visible light absorption and high photoluminescence. The novel three dimensional (3D) hierarchical ...C-dots-TiO2 (C-TiO2) heterojunctions were obtained by assembling the C-dots with end carboxylic acid groups on the surface of 3D TiO2 nanorod spheres, and confirmed by scanning electron microscopy and transmission electron microscopy analyses. The prepared C-dots show strong adsorption of rhodamine B (RhB) and very low activity in the photocatalytic degradation of RhB under visible light irradiation. The prepared 3D C-TiO2 composites show enhanced visible light absorption and higher surface area with the increase of C-dot ratios. The highest photocatalytic activity is obtained at the carbon weight percent of 3.6%, and the corresponding apparent first-order rate constant of 0.00616min−1 which is 1.8 times of that by the pure 3D TiO2 sphere. The mechanism shows that C-dots play an important role in the donation of excited electron to the conduction band of TiO2 under visible light irradiation.
A new class of chiral oxidants is developed. These readily accessible oxidants contain a nitro group for oxidation and a chiral sulfonamide moiety for stereoselectivity control. The chiral ...information from the oxidants can effectively transfer to the substrates in carbene-catalyzed β-hydroxylation of enals via single-electron-transfer radical processes. We expect these oxidants to find unique applications in other asymmetric oxidations and oxygen-atom-transferring reactions.
The deployment of wireless sensor networks, or WSNs, in industrial domains has attracted much attention over the past few years. An increasing number of applications have been developed such as for ...condition monitoring in the railway industry. Nevertheless, compared with traditional WSNs, the industrial environment is harsher, noisier, and more complex, which poses a higher requirement for the network security especially in terms of data trustiness and which further deters WSN practical integration in industrial applications. The main contribution of this research is to partially address the security issues by means of providing trusted data for industrial WSNs. To this end, a negative binomial distribution-based trust scheme combined with the D–S belief theory and a noise filter method is proposed and designed for industrial WSNs. In this paper, we first discuss the trust theory in WSNs and the disadvantages of traditional trust schemes for industrial applications, then analyze and evaluate the proposed method, and finally compare the performance of our method with some classic trust schemes. Through simulation tests about temperature readings of a factory workshop, it shows that the proposed method can improve the data trustiness, reliability, and robustness in the trust evaluation process under industrial environments and ensure the security of the network.
Chronic psychological stress is associated with an increased risk for relapse of inflammatory bowel diseases (IBD) and impedes the treatment of this condition. However, the impact of stress on the ...risk of IBD onset remains unclear. The goal of the present study was to examine whether chronic unpredictable mild stress (CUMS) could initiate or aggravate the onset of colon inflammation in rats which, in turn, would be capable of triggering bowel disease. We found that CUMS exposure increased infiltration of CD-45 positive cells and MPO activity, as well as augmented the expression of the inflammatory cytokines, IFN-γ and IL-6 within the colon of these rats. In addition, CUMS treatment changed the composition and diversity of gut microbiota and enhanced intestinal epithelial permeability, indicating the presence of a defect in the intestinal barrier. This CUMS-induced disruption of mucosal barrier integrity was associated with a reduction in expression of the tight junction protein, occludin 1, and an inhibition in mucosal layer functioning via reductions in goblet cells. Results from bacterial cultures revealed an increased presence of bacterial invasion after CUMS treatment as compared with that observed in controls. Thus, our data indicate that CUMS treatment induces alterations of the fecal microbiome and intestinal barrier defects, which facilitates bacterial invasion into colonic mucosa and further exacerbates inflammatory reactions within the colon. Accordingly, chronic stress may predispose patients to gastrointestinal infection and increase the risk of inflammation-related gut diseases.
Geochemical data is a typical geospatial-data with high-dimensional elemental properties. Existing methods for recognizing multivariate geochemical anomalies are limited in fully profiting from the ...inherent spatial-elemental structures of the geochemical data. This paper presents a novel method for the identification of multivariate geochemical anomalies, which leverages tensor dictionary learning over spatial-elemental dimensionalities to learn the joint representation of the spatial-elemental structures of geochemical data. The learned representations in spatial and elemental dictionaries are capable of capturing both the non-local features over space and the high-correlated distributed features across elements. The sparse representations over the spatial and elemental dimensions are exploited to reconstruct the geochemical background values, which permits us to identify multivariate geochemical anomalies considering the spatial-elemental structure of geochemical data. The proposed method is applied to recognize geochemical anomalies related to Au mineralization in the northwest Jiaodong Peninsula, Eastern China. By comparing with the anomalies recognized by deep autoencoder and local singularity analysis, we conclude that the tensor dictionary learning method is more effective to identify the mineralization-associated anomalies attribute to its ability to fuse spatial-elemental information of the geochemical data.
•A multivariate geochemical anomalies recognition method based on tensor dictionary learning.•Considering geochemical data as tensors to fully explore the spatial-elemental structures.•Spatial-elemental dictionaries capturing spatial and elemental features within the geological data.•Reconstruction of the geochemical background by leveraging sparsity across the spatial-elemental domain.
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