Common Node Research Articles

Knowledge graph based trajectory outlier detection in sustainable smart cities

Graph-based intelligent systems are emerging in the field of transportation systems. Knowledge graphs help to provide semantic and interconnectivity capabilities to the intelligent transportation system. In this paper, we propose a graph-based method for detecting outliers in the trajectory. Normal and outlier graphs are constructed using directed weighted graphs. Then, comparison with source and target graphs leading to vectors is performed. The similarity measure is used, which measures common nodes and edges. The features are then used by the machine learning based algorithm to classify the trajectory. Instead of a manually tuned parameter, the tree-based pipeline optimization method selects the best classifier and its hyperparameter. Then, the tuned model is compared with the traditional algorithms, i.e., random forest, decision tree, Naïve Bayes, and KNN. To evaluate the system under real conditions, an experiment is performed on a real dataset of trajectories. The results show that the graph-based method performs well and achieves an F-value of 0.81, while the optimization model achieves an F-value of 0.87. The graph-based model matched with the learning method helps to detect outliers and deviation points of the trajectory with high precision.

Deep Reinforcement Learning Based Optical and Acoustic Dual Channel Multiple Access in Heterogeneous Underwater Sensor Networks.

In this paper, we investigate how to efficiently utilize channel bandwidth in heterogeneous hybrid optical and acoustic underwater sensor networks, where sensor nodes adopt different Media Access Control (MAC) protocols to transmit data packets to a common relay node on optical or acoustic channels. We propose a new MAC protocol based on deep reinforcement learning (DRL), referred to as optical and acoustic dual-channel deep-reinforcement learning multiple access (OA-DLMA), in which the sensor nodes utilizing the OA-DLMA protocol are called agents, and the remainder are non-agents. The agents can learn the transmission patterns of coexisting non-agents and find an optimal channel access strategy without any prior information. Moreover, in order to further enhance network performance, we develop a differentiated reward policy that rewards specific actions over optical and acoustic channels differently, with priority compensation being given to the optical channel to achieve greater data transmission. Furthermore, we have derived the optimal short-term sum throughput and channel utilization analytically and conducted extensive simulations to evaluate the OA-DLMA protocol. Simulation results show that our protocol performs with near-optimal performance and significantly outperforms other existing protocols in terms of short-term sum throughput and channel utilization.

A retrospective study of small-pelvis radiotherapy plus image-guided brachytherapy in stage I-II non-bulky cervical squamous cell carcinoma.

We herein report a retrospective analysis of the efficacy of a combination therapy of pelvic irradiation that excluded the common iliac lymph nodes region and image-guided brachytherapy (IGBT) for non-bulky (≤4 cm) cervical cancer. Thirty-three patients with stage I–II cervical squamous cell carcinoma (≤4 cm) and without pelvic/para-aortic lymphadenopathy who were treated with definitive radiotherapy alone between February 2009 and September 2016 were included. The radiotherapy consisted of CT-based small-pelvis irradiation (whole pelvis minus common iliac lymph node area) of 20 Gy/10 fractions followed by pelvic irradiation with a midline block of 30 Gy/15 fractions and IGBT of 24 Gy/4 fractions (6 Gy/fraction for high-risk [HR] clinical target volume [CTV] D90%). In-room computed tomography (CT) imaging with applicator insertion was used for brachytherapy planning, with physical examinations and diagnostic magnetic resonance imaging (MRI) also being referred to for determination of HR CTV. Over a median follow-up of 60.5 months (range, 7–89), two patients developed distant recurrence and one developed local and distant recurrence. Two patients died from cervical cancer, one from hepatocellular carcinoma and one from non-cancerous disease. The 2/5-year local control (LC), progression-free survival (PFS) and overall survival (OS) rates were 100%/96.7%, 93.8%/90.6% and 93.9%/93.9%, respectively. No pelvic/para-aortic lymph node recurrence was observed. There were no late complications of grade 3 or higher in the small bowel, large bowel/rectum, or bladder. Our results suggest that a combination therapy of IGBT plus small-pelvis irradiation excluding common iliac lymph nodes provides reasonable clinical outcomes and can be a treatment option in non-bulky (≤4 cm) cervical squamous cell carcinoma.

A numerical model of grain growth

Abstract A numerical model for grain growth has been developed. The governing equations for the model are derived from a variational principle that enforces the balance between energy dissipation and changes of internal energy. This approach significantly simplifies previous modelling of grain growth by replacing a curved grain boundary with several straight lines and relaxing constraints on triple point angles. Such simplifications provide a basis for a finite element method by reducing the analysis of a complex network of interconnected curved lines to the motion of interacting points. In the finite element formulation two approaches are used, namely an element based formulation and a node based formulation. In the latter case, the properties of the elements are lumped together at their common nodes. Such an approach allows the nodal velocity to be determined independently of the forces on other nodes in a lumped mobility methodology, leading to a further reduction of computer time. Several planar grain networks of Voronoi polygons have been studied. The evolution of the grain structures determined from these two approaches is found to exhibit the same trends as those obtained using other models published in the literature. Owing to its simplicity, the node based formulation is recommended.

An effective community-based link prediction model for improving accuracy in social networks

Link prediction plays a predominant role in complex network analysis. It indicates to determine the probability of the presence of future links that depends on available information. The existing standard classical similarity indices-based link prediction models considered the neighbour nodes have a similar effect towards link probability. Nevertheless, the common neighbor nodes residing in different communities may vary in real-world networks. In this paper, a novel community information-based link prediction model has been proposed in which every neighboring node’s community information (community centrality) has been considered to predict the link between the given node pair. In the proposed model, the given social network graph can be divided into different communities and community centrality is calculated for every derived community based on degree, closeness, and betweenness basic graph centrality measures. Afterward, the new community centrality-based similarity indices have been introduced to compute the community centralities which are applied to nine existing basic similarity indices. The empirical analysis on 13 real-world social networks datasets manifests that the proposed model yields better prediction accuracy of 97% rather than existing models. Moreover, the proposed model is parallelized efficiently to work on large complex networks using Spark GraphX Big Data-based parallel Graph processing technique and it attains a lesser execution time of 250 seconds.

PARK2 regulates eIF4B-driven lymphomagenesis.

Patients with high-risk diffuse large B-cell lymphoma (DLBCL) have poor outcomes following first-line cyclophosphamide, doxorubicin, vincristine, prednisone, and rituximab (R-CHOP); thus, treatment of this fatal disease remains an area of unmet medical need and requires identification of novel therapeutic approaches. Dysregulation of protein translation initiation has emerged as a common downstream node in several malignancies, including lymphoma. Ubiquitination, a prominent post-translational modification associated with substrate degradation, has recently been shown to be a key modulator of nascent peptide synthesis by limiting several translational initiation factors. While a few deubiquitinases have been identified, the E3-ligase responsible for the critical ubiquitination of these translational initiation factors is still unknown. In this study, using complementary cellular models along with clinical readouts, we establish that PARK2 ubiquitinates eIF4B and consequently regulates overall protein translational activity. The formation of this interaction depends on upstream signaling, which is negatively regulated at the protein level of PARK2. Through biochemical, mutational, and genetic studies, we identified PARK2 as a mTORC1 substrate. mTORC1 phosphorylates PARK2 at Ser127, which blocks its cellular ubiquitination activity, thereby hindering its tumor suppressor effect on eIF4B's stability. This resultant increase of eIF4B protein level helps drive enhanced overall protein translation. These data support a novel paradigm in which PARK2-generated eIF4B ubiquitination serves as an anti-oncogenic intracellular inhibitor of protein translation, attenuated by mTORC1 signaling. Implications: Our data implicates the FASN/mTOR-PARK2-eIF4B axis as a critical driver of enhanced oncogene expression contributing to lymphomagenesis.

Hypobaric Hypoxia Induces Deficits in Adult Neurogenesis and Social Interaction via Cyclooxygenase-1/ EP1 Receptor Pathway Activating NLRP3 Inflammasome.

Low oxygen environments, like hypobaric hypoxia (HH), are common nodes in a number of diseases characterized by neuroinflammation, which is detrimental to the structural and functional aspects of hippocampal circuitry. Hypoxic conditions lead to elevation of inflammasome-mediated inflammation that may contribute to cognitive deficits. However, a systematic investigation of the impact of inflammasome-mediated neuroinflammation on the components of neurogenic niche during HH remains to be elusive. Cerebral hypoxia was induced in adult male Sprague Dawley rats via decreasing partial pressure of oxygen. The effect of HH (1, 3, and 7days at 25,000 ft) on social memory, anxiety, adult neurogenesis, and NLRP3- (NLR family pyrin domain containing 3) mediated neuroinflammation in the dentate gyrus (DG) was explored in detail. Furthermore, we explored the therapeutic efficacy of cyclooxygenase-1 inhibitor (valeryl salicylate, 5mg/kg/day, i.p.) and EP1 receptor (EP1R) antagonist (SC19220, 1mg/kg/day, i.p.) on HH-induced deficits. Seven days of HH exposure induced alteration in social and anxiety-like behavior along with perturbation in adult neurogenesis. Elevation in NLRP3, caspase-1, and IL-1β levels was observed during HH from day 1. A notable increase in the COX-1/EP1R pathway in activated glial cells in DG was evident during HH. COX-1 inhibitor and EP1R antagonist mitigated the detrimental effects of HH on social memory, adult neurogenesis via blunting NLRP3-mediated inflammation. Our data showed induction of the COX-1/EP1R pathway in the glial cells, which is detrimental to neurogenesis and social memory, opening up the possibility that the COX-1/EP1R pathway is a plausible target for inflammasome-related neurogenesis impairments.

Research on Wireless Sensor Network Access Control and Load Balancing in the Industrial Digital Twin Scenario

Wireless sensor networks which are based on distributed information processing technology are taking an increasingly key role in industrial digital twin scenarios. There are many important issues in the access of networks. One of the most important issues is how to improve network access control and the effectiveness of load balancing. Based on the industrial digital twin technology, this article first introduces several typical network access and network loads and performs tree-structured processing on the outliers generated during the chain formation process to reduce the length of the data transmission path, optimize the main chain head and subchain chain head selection strategy and chaining rules, and perform nonchain operations on common nodes and chain heads near sink to reduce data inverse transfer. The experimental results show that this paper uses the digital twin calculation formula to accurately and objectively determine the remaining cluster head and the distance head and the base station, so that when the node distance is limited, the network energy consumption can be balanced as much as possible, and the network load is promoted.

The Clinical Aspects and Prognostic Factors Concerning Survival in Patients With Recurrent Cervical Cancer After Radical Hysterectomy and Adjuvant Chemoradiotherapy.

PurposeTo investigate the recurrence patterns and prognostic factors of patients with recurrent cervical cancer after radical hysterectomy with node dissection (RHND) followed by adjuvant radiotherapy (RT)/concurrent chemoradiotherapy (CCRT).MethodsThe medical records of 153 patients with pre-operative International Federation of Gynecology and Obstetrics stage IB-IIA cervical cancer, who were treated with RHND followed by adjuvant RT/CCRT at the Liaoning Cancer Hospital between January 1, 2012 and May 31, 2018, were retrospectively analyzed.ResultsThe median disease progression-free survival time was 16 months, and 75.2% (115/153) of patients had a relapse within two years. The survival of patients with multi-site relapse was significantly lower in comparison to those with relapse in a single site (p < 0.001). The survival rate of patients with distant metastasis (DM) and combined recurrence (DM with localregional recurrence [LR]) was significantly lower than that of patients with only LR (p = 0.006, p < 0.001). Furthermore, the survival rate of patients with combined recurrence was significantly lower than that of patients with only DM (p = 0.046). Multivariate analysis showed that resection margin involvement, para-aortic and common iliac lymph node metastasis, DM, no treatment after disease relapse, and early disease relapse were independent prognostic factors associated with poor survival.ConclusionMost of the cervical cancer patients who received initial RHND followed by adjuvant RT/CCRT had a relapse within two years. Resection margin involvement, para-aortic and common iliac lymph node metastasis, DM, no treatment after recurrence, and early disease relapse were found to be prognostic factors in patients with recurrent cervical cancer after RHND followed by adjuvant RT/CCRT.

Thrombocytopenia in COVID‑19 and vaccine‑induced thrombotic thrombocytopenia.

The highly heterogeneous symptomatology and unpredictable progress of COVID-19 triggered unprecedented intensive biomedical research and a number of clinical research projects. Although the pathophysiology of the disease is being progressively clarified, its complexity remains vast. Moreover, some extremely infrequent cases of thrombotic thrombocytopenia following vaccination against SARS-CoV-2 infection have been observed. The present study aimed to map the signaling pathways of thrombocytopenia implicated in COVID-19, as well as in vaccine-induced thrombotic thrombocytopenia (VITT). The biomedical literature database, MEDLINE/PubMed, was thoroughly searched using artificial intelligence techniques for the semantic relations among the top 50 similar words (>0.9) implicated in COVID-19-mediated human infection or VITT. Additionally, STRING, a database of primary and predicted associations among genes and proteins (collected from diverse resources, such as documented pathway knowledge, high-throughput experimental studies, cross-species extrapolated information, automated text mining results, computationally predicted interactions, etc.), was employed, with the confidence threshold set at 0.7. In addition, two interactomes were constructed: i) A network including 119 and 56 nodes relevant to COVID-19 and thrombocytopenia, respectively; and ii) a second network containing 60 nodes relevant to VITT. Although thrombocytopenia is a dominant morbidity in both entities, three nodes were observed that corresponded to genes (AURKA, CD46 and CD19) expressed only in VITT, whilst ADAM10, CDC20, SHC1 and STXBP2 are silenced in VITT, but are commonly expressed in both COVID-19 and thrombocytopenia. The calculated average node degree was immense (11.9 in COVID-19 and 6.43 in VITT), illustrating the complexity of COVID-19 and VITT pathologies and confirming the importance of cytokines, as well as of pathways activated following hypoxic events. In addition, PYCARD, NLP3 and P2RX7 are key potential therapeutic targets for all three morbid entities, meriting further research. This interactome was based on wild-type genes, revealing the predisposition of the body to hypoxia-induced thrombosis, leading to the acute COVID-19 phenotype, the 'long-COVID syndrome', and/or VITT. Thus, common nodes appear to be key players in illness prevention, progression and treatment.

A 65 nm Duplex Transconductance Path Up-Conversion Mixer for 24 GHz Automotive Short-Range Radar Sensor Applications.

A 24 GHz highly-linear upconversion mixer, based on a duplex transconductance path (DTP), is proposed for automotive short-range radar sensor applications using the 65-nm CMOS process. A mixer with an enhanced transconductance stage consisting of a DTP is presented to improve linearity. The main transconductance path (MTP) of the DTP includes a common source (CS) amplifier, while the secondary transconductance path (STP) of the DTP is implemented as an improved cross-quad transconductor (ICQT). Two inductors with a bypass capacitor are connected at the common nodes of the transconductance stage and switching stage of the mixer, which acts as a resonator and helps to improve the gain and isolation of the designed mixer. According to the measured results, at 24 GHz the proposed mixer shows that the linearity of output 1-dB compression point (OP1dB) is 3.9 dBm. And the input 1-dB compression point (IP1dB) is 0.9 dBm. Moreover, a maximum conversion gain (CG) of 2.49 dB and a noise figure (NF) of 3.9 dB is achieved in the designed mixer. When the supply voltage is 1.2 V, the power dissipation of the mixer is 3.24 mW. The mixer chip occupies an area of 0.42 mm.

Multiple‐GPU parallelization of three‐dimensional material point method based on single‐root complex

AbstractAs one of the arbitrary Lagrangian–Eulerian methods, the material point method (MPM) owns intrinsic advantages in simulation of large deformation problems by combining the merits of the Lagrangian and Eulerian approaches. Significant computational intensity is involved in the calculations of the MPM due to its very fine mesh needed to achieve a sufficiently high accuracy. A new multiple‐GPU parallel strategy is developed based on a single‐root complex architecture of the computer purely within a CUDA environment. Peer‐to‐Peer (P2P) communication between the GPUs is performed to exchange the information of the crossing particles and ghost element nodes, which is faster than the heavy send/receive operations between different computers through the infiniBand network. Domain decomposition is performed to split the whole computational task over the GPUs with a number of subdomains. The computations within each subdomain are allocated on a corresponding GPU using an enhanced “Particle‐List” scheme to tackle the data race during the interpolation from associated particles to common nodes. The acceleration effect of the parallelization is evaluated with two benchmarks cases, mini‐slump test after a dam break and cone penetration test in clay, where the maximum speedups with 1 and 8 GPUs are 88 and 604, respectively.

Significance of the dissection of common hepatic arterial lymph nodes in patients with oesophageal carcinoma: a multicentre retrospective study

ObjectivesTo explore the significance of intraoperative common hepatic arterial lymph node dissection in patients with ooesophageal squamous carcinoma (ESCC) without coeliac trunk lymph node metastasis indicated by abdominal enhanced CT.MethodsPatients...

Quality and Energy Aware Multipath Routing in Wireless Multimedia Sensor Networks

In Wireless Multimedia Sensor Networks (WMSNs), the sensor nodes are capable of acquiring the multimedia information like wildlife tracking, traffic accidents as well scalar data. Because of these factors, the WMSNs have a bright future in both academic and industry circles. Multimedia applications, on the other hand, generate a lot of network traffic, which causes a lot of delay and wastes a lot of energy. In WMSNs, both network duration and quality of service are critical, hence an effective routing algorithm that can manage more data while simultaneously extending network lifetime is necessary. Towards this objective, in this paper, we proposes a Quality Aware Multipath Routing (QAMR) that selects multiple paths based on three reference metrics; they are Expected Transmission Count, Energy and Delay. For a given source and sink node, the QAMR constructs a composite metric by combining these three metrics and selects node disjoint multiple paths. Since the multiple paths with common nodes has less efficiency in data forwarding, we opt for node disjoint paths in which no two paths have one common node. On the proposed system QAMR model, extensive simulation tests are performed, and the outcomes are evaluated using a variety of metrics such as energy consumption, throughput, delay, and packet delivery ratio.

Boundary stabilization for a star-shaped network of variable coefficients strings linked by a point mass

&lt;p style='text-indent:20px;'&gt;This study is concerned with the pointwise stabilization for a star-shaped network of &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ N $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; variable coefficients strings connected at the common node by a point mass and subject to boundary feedback dampings at all extreme nodes. It is shown that the closed-loop system has a sequence of generalized eigenfunctions which forms a Riesz basis for the state Hilbert space. As a consequence, the spectrum-determined growth condition fulfills. In the meanwhile, the asymptotic expression of the spectrum is presented, and the exponential stability of the system is obtained by giving the optimal decay rate. We prove also that a phenomenon of lack of uniform stability occurs in the absence of damper at one extreme node. This paper reconfirmed the main stability results given by Hansen and Zuazua [SIAM J. Control Optim., &lt;b&gt;33&lt;/b&gt; (1995), 1357-1391] in a very particular case.&lt;/p&gt;

Prioritizing Candidate Genes for Type 2 Diabetes Mellitus using Integrated Network and Pathway Analysis.

Background:Type 2 Diabetes Mellitus (T2DM) has emerged as a major threat to global health that fosters life-threatening clinical complications, taking a huge toll on our society. More than 65 million Indians suffer from T2DM, making it one of the leading causes of death. T2DM and associated complications have to be constantly monitored and managed which reduces the overall quality of life and increases socioeconomic burden. Therefore, it is crucial to develop specific treatment and management strategies. In order to achieve this, it is essential to understand the underlying genetic causes and molecular mechanisms.Methods:Integrated gene network and ontology analyses facilitate prioritization of plausible candidate genes for T2DM and also aid in understanding their mechanistic pathways. In this study, T2DM-associated genes were subjected to sequential interaction network and gene set enrichment analysis. High ranking network clusters were derived and their interrelation with pathways was assessed.Results:About 23 significant candidate genes were prioritized from 615 T2DM-associated genes which were overrepresented in pathways related to insulin resistance, type 2 diabetes, signaling cascades such as insulin receptor signaling pathway, PI3K signaling, IGFR signaling pathway, ERBB signaling pathway, MAPK signaling pathway and their regulatory mechanisms.Conclusion:Of these, two tyrosine kinase receptor genes-EGFR and IGF1R were identified as common nodes and can be considered to be significant candidate genes in T2DM.

Dominacija na petougaonim kaktus-lancima

Introduction/purpose: A graph as a mathematical object occupies a special place in science. Graph theory is increasingly used in many spheres of business and scientific fields. This paper analyzes pentagonal cactus chains, a special type of graphs composed of pentagonal cycles in which two adjacent cycles have only one node in common. The aim of the research is to determine the dominant set and the dominance number on ortho and meta pentagonal cactus chains. Methods: When the corresponding destinations are treated as graph nodes and the connections between them as branches in the graph, the complete structure of the graph is obtained, to which the laws of graph theory are applied. The vertices of the pentagon are treated as nodes of the graph and the sides as branches in the graph. By applying mathematical methods, the dominance was determined on one pentagon, then on two pentagons with a common node, and then on ortho and meta pentagonal cactus chains. Results: The research has shown that the dominance number on the ortho chain 𝑂ℎ of the length h ≥ 2 is equal to the value of the expression ⌈ 3ℎ 2 ⌉ while on the meta chain 𝑀ℎ it is equal to the value of the expression h+1, which was proven in this paper. Conclusion: The results show that the dominant sets and the dominance numbers on ortho and meta pentagonal cactus chains are determined and explicitly expressed by mathematical expressions. They also point to the possibility of their application in the fields of science as well as in the spheres of business in which these structures appear.

Study on abnormal data acquisition method of industrial internet of things communication based on node clustering

In order to overcome the problems of low efficiency and poor accuracy of traditional abnormal data capture, this paper proposes an industrial IoT communication abnormal data capture method based on node clustering. First, analyse the structure of Industrial Internet of Things (IIoT), collect communication data and extract the characteristics of industrial communication data; then, the node subordination function is constructed, and the abnormal data types are divided by common neighbour node analysis. Finally, the matching degree between features is extracted through node clustering calculation, and the matching degree is input into the classifier function and the communication exception data capture result is output. The results show that the malicious node capture rate of this method is always higher than 92%, the capture time is always less than 3.6 seconds and the effect of abnormal data capture of IoT communication is improved.

Core-based Approach to Measure Pairwise Layer Similarity in Multiplex Network

Most of the recent works on network science are focused on investigating various interactions among a set of entities present in a system that can be represented by multiplex network. Each type of relationship is treated as a layer of multiplex network. Some of the recent works on multiplex networks are focused on deriving layer similarity from node similarity where node similarity is evaluated using neighborhood similarity measures like cosine similarity and Jaccard similarity. But this type of analysis lacks in finding the set of nodes having the same influence in both the network. The discovery of influence similarity between the layers of multiplex networks helps in strategizing cascade effect, influence maximization, network controllability, etc. Towards this end, this paper proposes a pairwise similarity evaluation of layers based on a set of common core nodes of the layers. It considers the number of nodes present in the common core set, the average clustering coefficient of the common core set, and fractional influence capacity of the common core set to quantify layer similarity. The experiment is carried out on three real multiplex networks. As the proposed notion of similarity uses a different aspect of layer similarity than the existing one, a low positive correlation (close to non-correlation) is found between the proposed and existing approach of layer similarity. The result demonstrates that the degree of coreness difference is less for the datasets in the proposed method than the existing one. The existing method reports the coreness difference to be 40% and 18.4% for the datasets CS-AARHUS and EU-AIR TRANSPORTATION MULTIPLEX respectively whereas it is found to be 20% and 8.1% using proposed approach.

Is Megamerger Better?–Based on the Link Prediction Perspective

Link prediction provides insight into the evolutionary mechanisms of complex networks by predicting missing edges. Existing research has proposed many similarity algorithms based on local information, and some link prediction algorithms typically perform better in different networks. It is generally believed that a megamerger is beneficial. In the perspective of link prediction, merging the good-performing algorithms brings higher prediction accuracy. And the more times the experiment is executed, the higher the accuracy of link prediction. Therefore, this research proposes a new link prediction algorithm based on the theory of megamerger in management and the concept of partnership, and uses ten actual complex networks for experiments to test the above two hypotheses. The experimental results show that megamerger is not applicable to the link prediction algorithm. In addition, there is no positive correlation between the increasing the quantity of experiments and improving the accuracy of the experiments, so the above two hypotheses are rejected. Hence, this research presumes that megamerger of the comprehensive information of the network, such as the resource flow between nodes, the degree of common neighbor nodes, and partnership of nodes, does not improve the accuracy of link prediction. For a refined network with a small number of nodes and a short average path length, it is recommended that the quantity of experiments be set to only ten can achieve the required accuracy of link prediction.