In opportunistic networks, opportunistic routing protocols are used for nodes to forward messages, which are the core technology to realize various opportunistic network applications. However, the ...existing opportunistic routing protocols generally have three problems: Key nodes forwarded too many messages, resulting in unbalanced energy consumption; Delivered message still occupies the cache space of nodes, resulting in the loss of undelivered message; Nodes constantly sense their surroundings and forward messages, resulting in rapid energy depletion. To better solve these problems, this paper proposes HP-ECD: heuristic Prophet routing protocol based on energy balance, cache optimization, and asynchronous dormancy. Based on Prophet, First, the HP-ECD defines the message forwarding benefits based on multiple node attributes and message importance, to realize the energy balance mechanism of nodes. Then, the HP-ECD designs the message delivery list based on the node history information, to realize the cache optimization mechanism of nodes. Finally, the HP-ECD determines whether a node is in dormancy based on the node running status, to realize the asynchronous dormancy mechanism of nodes. Simulation results show that, compared with Epidemic, Prophet, and EC-CW, HP-ECD greatly improves the message delivery rate and average remaining energy, and reduces the routing overhead rate and average storage time.
Ag₂S quantum dots were dispersed on the surface of SnS₂ nanoflowers forming a heterojunction via in-situ ion exchange to improve photocatalytic degradation of RhB. All samples exhibit the hexagonal ...wurtzite structure. The size of Ag₂S@SnS₂ composites are ~ 1.5 μm flower-like with good crystallinity. Meanwhile, the E
of 3% Ag₂S@SnS₂ is close to that of pure SnS₂. Consequently, the 3% Ag₂S@SnS₂ composite displays the excellent photocatalytic performance under simulated sunlight irradiation with good cycling stability, compared to the pure SnS₂ and other composites. Due to the blue and yellow luminescence quenching, the photogenerated electrons and holes is effectively separated in the 3% Ag₂S@SnS₂ sample. Especially, the hydroxyl radicals and photogenerated holes are main active species.
Patients with multiple sclerosis (MS) suffer from repetitive neurological deterioration, while anxiety may play a significant role in the disease's progression.
To explore the prevalence of anxiety ...in MS and to investigate the risk factors related to anxiety in MS patients.
An analysis of four databases, PubMed, Web of Science, EMBASE, and Cochrane Library, has been conducted to determine the prevalence or risk factors for anxiety in MS published before May 2021.
In total, 32 studies were found to be eligible. Anxiety prevalence was estimated to be 36% based on the pooled estimates the 95% confidence interval (CI) = 0.30-0.42,
= 98.4%. Significant risk factors for developing of anxiety were as follows: age at survey the weighted mean difference (WMD) = 0.96, 95% CI = 0.86-1.06,
= 43.8%, female the odd ratio (OR) = 1.78, 95% CI = 1.38-2.30,
= 0%, living together (OR 2.83, 95% CI = 1.74-4.59,
= 0%), past psychiatric history (OR 2.42, 95% CI = 1.56-3.75,
= 0%), depression (OR 7.89, 95% CI = 3.71-16.81,
= 0%), not taking MS medication (OR 2.33, 95% CI = 1.29-4.21,
= 77.8%), relapsing-remitting MS (RRMS) (OR 1.50, 95% CI = 0.94-2.37,
= 53.5%), and baseline Expanded Disability Status Scale (EDSS) (OR 0.84, 95% CI = 0.48-1.21,
= 62.2%).
An estimated 36% of people with MS suffer from anxiety. And anxiety rates in MS patients are significantly associated with age, gender, living together, prior psychiatric history, depression, drug compliance, RRMS, and baseline EDSS.
https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=287069, identifier CRD42021287069.
Fairness Comparison of Uplink NOMA and OMA Zhiqiang Wei; Jiajia Guo; Ng, Derrick Wing Kwan ...
2017 IEEE 85th Vehicular Technology Conference (VTC Spring),
2017-June
Conference Proceeding
Odprti dostop
In this paper, we compare the resource allocation fairness of uplink communications between non-orthogonal multiple access (NOMA) schemes and orthogonal multiple access (OMA) schemes. Through ...characterizing the contribution of the individual user data rate to the system sum rate, we analyze the fundamental reasons that NOMA offers a more fair resource allocation than that of OMA in asymmetric channels. Furthermore, a fairness indicator metric based on Jain's index is proposed to measure the asymmetry of multiuser channels. More importantly, the proposed metric provides a selection criterion for choosing between NOMA and OMA for fair resource allocation. Based on this discussion, we propose a hybrid NOMA-OMA scheme to further enhance the users fairness. Simulation results confirm the accuracy of the proposed metric and demonstrate the fairness enhancement of the proposed hybrid NOMA-OMA scheme compared to the conventional OMA and NOMA schemes.
As we all know, semantic segmentation of remote sensing (RS) images is to classify the images pixel by pixel to realize the semantic decoupling of the images. Most traditional semantic decoupling ...methods only decouple and do not perform scale-separation operations, which leads to serious problems. In the semantic decoupling process, if the feature extractor is too large, it will ignore the small-scale targets; if the feature extractor is too small, it will lead to the separation of large-scale target objects and reduce the segmentation accuracy. To address this concern, we propose a scale-separated semantic decoupled transformer (SSDT), which first performs scale-separation in the semantic decoupling process and uses the obtained scale information-rich semantic features to guide the Transformer to extract features. The network consists of five modules, scale-separated patch extraction (SPE), semantic decoupled transformer (SDT), scale-separated feature extraction (SFE), semantic decoupling (SD), and multiview feature fusion decoder (MFFD). In particular, SPE turns the original image into a linear embedding sequence of three scales; SD divides pixels into different semantic clusters by K-means, and further obtains scale information-rich semantic features; SDT improves the intraclass compactness and interclass looseness by calculating the similarity between semantic features and image features, the core of which is decoupled attention. Finally, MFFD is proposed to fuse salient features from different perspectives to further enhance the feature representation. Our experiments on two large-scale fine-resolution RS image datasets (Vaihingen and Potsdam) demonstrate the effectiveness of the proposed SSDT strategy in RS image semantic segmentation tasks.
In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown ...excellent performance in various situations. However, the compound-protein interaction is complicated and the features extracted by most deep models are not comprehensive, which limits the performance to a certain extent. In this paper, we proposed a multiscale convolutional network that extracted the local and global features of the protein and the topological feature of the compound using different types of convolutional networks. The results showed that our model obtained the best performance compared with the existing deep learning methods.
Molecular toxicity prediction is one of the key studies in drug design. In this paper, a deep learning network based on a two-dimension grid of molecules is proposed to predict toxicity. At first, ...the van der Waals force and hydrogen bond were calculated according to different descriptors of molecules, and multi-channel grids were generated, which could discover more detail and helpful molecular information for toxicity prediction. The generated grids were fed into a convolutional neural network to obtain the result. A Tox21 dataset was used for the evaluation. This dataset contains more than 12,000 molecules. It can be seen from the experiment that the proposed method performs better compared to other traditional deep learning and machine learning methods.
Neuropathic pain is a debilitating pathological condition that presents significant therapeutic challenges in clinical practice. Unfortunately, current pharmacological treatments for neuropathic pain ...lack clinical efficacy and often lead to harmful adverse reactions. As G protein-coupled receptors (GPCRs) are widely distributed throughout the body, including the pain transmission pathway and descending inhibition pathway, the development of novel neuropathic pain treatments based on GPCRs allosteric modulation theory is gaining momentum. Extensive research has shown that allosteric modulators targeting GPCRs on the pain pathway can effectively alleviate symptoms of neuropathic pain while reducing or eliminating adverse effects. This review aims to provide a comprehensive summary of the progress made in GPCRs allosteric modulators in the treatment of neuropathic pain, and discuss the potential benefits and adverse factors of this treatment. We will also concentrate on the development of biased agonists of GPCRs, and based on important examples of biased agonist development in recent years, we will describe universal strategies for designing structure-based biased agonists. It is foreseeable that, with the continuous improvement of GPCRs allosteric modulation and biased agonist theory, effective GPCRs allosteric drugs will eventually be available for the treatment of neuropathic pain with acceptable safety.
The allosteric modulation based on four key GPCRs (μ-OR, A1R, mGluRs and CBRs) shows promise as a therapeutic strategy for neuropathic pain. Allosteric modulators effectively mitigate non-specific side effects by modulating the affinity or efficacy of the orthosteric ligands, or through biased signaling effects. Display omitted
For the first time we synthesized the fire history for the eastern monsoonal region of China during the Holocene, through the combined analyses of paleofire indices including both charcoal and black ...carbon records from 14 localities available at present. Our results show that in eastern China fire activity was relatively higher before 9500 cal yr BP, very low between 9500 and 7500 cal yr BP, and evidently increased since 7500 cal yr BP, reaching its maximum at about 2000 cal yr BP. This general pattern of fire activity closely follows the Holocene effective moisture evolution in the eastern monsoonal China at an orbital timescale, i.e. more fires occurred during drier conditions, while less fire occurred during more humid conditions. These results suggest that the fire activity in eastern China were primarily driven by climate changes (i.e., variations of past moisture conditions) and monsoon-related changes in vegetation communities, ultimately forced by the Northern Hemisphere insolation (NHI) on orbital timescales over the Holocene. Our data also show that the fire activity increased concurrently with significant human development from the mid- to late-Holocene, possibly suggesting an intensified trend of human-induced fire activities since about 7500 years ago. Moreover, the fire activity in eastern China closely parallels the atmospheric CO2 concentration inferred from Antarctica ice-cores. Our synthesis will help to better understand the relationship between fire, climate and human activity at a variety of geographical scales.
•Fire activity in eastern China was primarily driven by Holocene climate changes.•Human impacts on Holocene fire activity cannot be ruled out.•Fire history in eastern China parallels global atmospheric CO2 concentration.
PDF
