Initial Node Research Articles

A machine-learning-based combination of criteria to detect bladder cancer lymph node metastasis on [18F]FDG PET/CT: a pathology-controlled study.

Initial pelvic lymph node (LN) staging is pivotal for treatment planification in patients with muscle-invasive bladder cancer (MIBC), but [18F]FDG PET/CT provides insufficient and variable diagnostic performance. We aimed to develop and validate a machine-learning-based combination of criteria on [18F]FDG PET/CT to accurately identify pelvic LN involvement in bladder cancer patients. Consecutive patients with localized MIBC who performed preoperative [18F]FDG PET/CT between 2010 and 2017 were retrospectively assigned to training (n = 129) and validation (n = 44) sets. The reference standard was the pathological status after extended pelvic LN dissection. In the training set, a random forest algorithm identified the combination of criteria that best predicted LN status. The diagnostic performances (AUC) and interrater agreement of this combination of criteria were compared to a consensus of experts. The overall prevalence of pelvic LN involvement was 24% (n = 41/173). In the training set, the top 3 features were derived from pelvic LNs (SUVmax of the most intense LN, and product of diameters of the largest LN) and primary bladder tumor (product of diameters). In the validation set, diagnostic performance did not differ significantly between the combination of criteria (AUC = 0.59 95%CI [0.43-0.73]) and the consensus of experts (AUC = 0.64 95%CI [0.48-0.78], p = 0.54). The interrater agreement was equally good with Κ = 0.66 for both. The developed machine-learning-based combination of criteria performs as well as a consensus of experts to detect pelvic LN involvement on [18F]FDG PET/CT in patients with MIBC. • The developed machine-learning-based combination of criteria performs as well as experts to detect pelvic LN involvement on [18F]FDG PET/CT in patients with muscle-invasive bladder cancer. • The top 3 features to predict LN involvement were the SUVmax of the most intense LN, the product of diameters of the largest LN, and the product of diameters of the primary bladder tumor.

Multiplex network community detection algorithm based on motif awareness

Multiplex networks have shown high utility in modeling complex systems, e.g., academic social networks. Composite community structures contribute to revealing meaningful grouping patterns in multiplex networks. Multiplex network community detection, especially using higher-order interaction information, has become a popular research topic in complex network analysis. We propose a multiplex network community detection algorithm based on motif awareness (CDMA) to reduce information loss during the aggregation of multiplex networks, which can solve the community detection problem for cold start nodes and improve the quality of multiplex network community detection. Interlayer differences are fully considered in the topology of the multiplex network throughout the algorithm. First, the motif is used to mine the higher-order interaction information in each layer of the network topology, and a multiplex network aggregation model is constructed to aggregate the multiplex network into a single-layer composite network. In addition, the information propagation method is used to transform the node information in the single-layer composite network into a vector. Furthermore, we propose a node importance evaluation index and select the nodes with the greatest significance to solve the initial node selection sensitivity problem of the k-means algorithm. The comparative experimental results on real multiplex network datasets, e.g., SCHOLAT, show that the proposed CDMA algorithm can play an important role in addressing the issue of effective detection of the community structure of multiplex networks with higher accuracy.

Data forwarding: A new VoteRank and Assortativity based approach to improve propagation time in social networks

With the rapid development of social networks, studying and analyzing their structures and behaviors has become one of the most important requirements of businesses. Social network analysis can be used for many different purposes such as product ads, market orientation detection, influential members detection, predicting user behaviors, recommender systems improvements, etc. One of the newest research topics in social network analysis is the enhancement of the information propagation performance in different aspects based on application. In this paper, a new method is proposed to improve few metrics such as distribution time and precision on social networks. In this method, the local attributes of nodes and also the structural information of the network is used to forward data across the network and reduce the propagation time. First of all, the centrality and Assortativity are calculated for all nodes separately to select two sets of nodes with the highest values for both criteria. Then, the initial active nodes of the network are selected by calculating the intersection of the two sets. Next, the distribution paths are detected based on the initial active nodes to calculate the propagation time. The performance analysis results show that the proposed method has better outcomes in comparison to other state-of-the-art methods in terms of distribution time, precision, recall, and AUPR criteria.

Analytical results for the distribution of first-passage times of random walks on random regular graphs

We present analytical results for the distribution of first-passage (FP) times of random walks (RWs) on random regular graphs that consist of N nodes of degree c ⩾ 3. Starting from a random initial node at time t = 0, at each time step t ⩾ 1 an RW hops into a random neighbor of its previous node. In some of the time steps the RW may hop into a yet-unvisited node while in other time steps it may revisit a node that has already been visited before. We calculate the distribution P(T FP = t) of first-passage times from a random initial node i to a random target node j, where j ≠ i. We distinguish between FP trajectories whose backbone follows the shortest path (SPATH) from the initial node i to the target node j and FP trajectories whose backbone does not follow the shortest path (¬SPATH). More precisely, the SPATH trajectories from the initial node i to the target node j are defined as trajectories in which the subnetwork that consists of the nodes and edges along the trajectory is a tree network. Moreover, the shortest path between i and j on this subnetwork is the same as in the whole network. The SPATH scenario is probable mainly when the length ℓ ij of the shortest path between the initial node i and the target node j is small. The analytical results are found to be in very good agreement with the results obtained from computer simulations.

Substantiating the methods for calculating the split cylindrical drums of mine hoisting machines with increased rope capacity

Purpose. A simplified calculation method development for strengthened split cylindrical drum structures of the mine hoisting machines of the TsR-6.756.2/1.95 type. Methodology. The drum structure for the mine hoisting machines is conventionally divided into several nodes. A simplified averaged model is constructed for the nodes consisting of the shell, frontal, rib strengthening and brake discs (end nodes) based on the analysis of their operation, in particular, the stiffness analysis under different loads. After that, when assembling, the initial drum nodes are replaced with simplified ones and a so-called simplified model for the whole drum is constructed, as well as the displacements of the brake disc edges are determined. Findings. The simplified models for drum nodes have been created based on the analysis of their operation, and then the displacements of the whole drum simplified model have been calculated. Originality. The simplified calculation method error has been estimated: the method of averaging with increased thickness of the frontals. Practical value. For the TsR-6.756.2/1.95 hoisting machine with a drum diameter of 6750 mm, a drum width of 6200 mm and an adjustable part width of 1950 mm, with a groove cutting pitch of 51 mm and a maximum lifting depth of 1477 m, it has been determined that the maximum axial displacements for brake disc edges of the jammed and adjustable parts are 0.854 and 1.921mm, respectively. A simplified calculation method has been developed for strengthened split cylindrical drum structures of the mine hoisting machines of the TsR-6.756.2/1.95 type, available for use in middle-class packages such as SolidWorks Simulation.

Patterns of Failure in Resectable Stage I-IIIA NSCLC Treated with Neoadjuvant Immunotherapy Combinations, a Secondary Analysis of a Prospective Trial

<h3>Purpose/Objective(s)</h3> The impact of neoadjuvant immune checkpoint inhibitor (ICI)-based therapies on mediastinal recurrence patterns after resection for non-small cell lung cancer (NSCLC) is unknown. We describe patterns of mediastinal failure after receipt of neoadjuvant ICI and provide a descriptive analysis of patients who experienced a mediastinal recurrence. <h3>Materials/Methods</h3> Between August 2015 and August 2021, patients with stage I-IIIA NSCLC treated with nivolumab-based therapies prior to resection on the clinical trial NA_00092076 were identified. Patient, tumor, treatment, pathological, recurrences, and post-operative therapy data were obtained from the trial. Variables including central location, initial mediastinal node involvement were obtained via chart review. <h3>Results</h3> Of the 23 NSCLC patients identified: median age was 66 years (range: 48-78), 68% (n= 15) were male, and 39% (n=9) and 35% (n=8) had stage II and III NSCLC, respectively at initial diagnosis. Histological subtypes included adenocarcinoma (50%) and squamous (36%). ICI therapies included: nivolumab (48%, n=11), nivolumab/ipilimumab (17%, n=4), nivolumab with carboplatin/paclitaxel (4%, n=1). A majority of patients received a lobectomy (70%, n=16) and had a margin negative resection (95%, n=22). The median follow-up was 41 months (range: 1 – 74). A majority (70%, n=13) of patients had centrally located tumors. Nodal involvement included: 57% (n=13) N0, 35% (n=7) N1, and 13% (n=3) N2 disease. Two patients developed mediastinal recurrence, both with initial stage III disease. Of the 3 patients with cN2 disease, none received post-operative radiation (RT) after resection and 33% (n=1/3) developed mediastinal recurrence. Of the two patients who experience a mediastinal recurrence: Patient 1 (initial T1N2 disease) achieved major pathologic response (MPR) and overall tumor downstaging to ICI. Locoregional recurrence included the initially involved 4R node. The patient is without evidence of disease 4 years after salvage chemoRT. Patient 2 (initial T3N1 disease) had no MPR nor tumor down staging to ICI. This patient developed mediastinal and distant metastases after declining post-op RT after a R1 pneumonectomy. Both patients received neoadjuvant ICI alone and had initial tumor disease within 2 cm of central airways. <h3>Conclusion</h3> In this analysis, we provide the first report of patterns of mediastinal failure in patients with resectable stage I-IIIA NSCLC who have received neoadjuvant ICI-based therapies. Patients with MPR may still have risk of mediastinal recurrence. The patients who developed mediastinal failure in this cohort had initial N2 disease, lack of response to therapy, and R1 resection. These variables may be associated with mediastinal failure risk after neoadjuvant ICI-based therapies. Multi-institutional efforts are needed to better characterize the risk factors associated with mediastinal recurrence after neoadjuvant ICI.

Proton SBRT for Isolated Nodal Recurrence in Gynecological Cancers

<h3>Purpose/Objective(s)</h3> Stereotactic body radiotherapy (SBRT) for isolated lymph node recurrence has been shown to have good rates of local control (LC) in gynecological cancers. However there exists the risk of toxicities to nearby organs. This single institution, retrospective outcome and dosimetric analysis assesses treatment response, associated GI and GU toxicities and associated characteristics for improved or worsened outcomes with proton based SBRT, hypothesizing that there will be low rates of toxicities while maintaining beneficial outcomes. <h3>Materials/Methods</h3> Adult patients diagnosed with primary gynecologic malignancy with isolated nodal recurrence treated with proton SBRT from 2006-2021 at a single institution were included. Toxicities were evaluated using RTOG/EORTC and CTCAE grading. Descriptive statistics were used to describe characteristics associated with local recurrence, toxicity, and survival. <h3>Results</h3> Fourteen patients with 21 nodal lesions were treated with 19 courses of proton SBRT. Median age was 64.5 (range: 47-78) years at time of initial cancer diagnosis and median of 66.0 (range: 51- 80) years at initial lymph node recurrence. Patients recurred within an average of 13.00 (range: 0.66- 31.41) months from end of initial treatment. Primary cancer sites included endometrium (n= 9), cervix (n= 2), ovary (n= 2), and vagina (n= 2). SBRT doses ranged from 30- 50 Gy (median 40 Gy) in 5-15 fractions with mean BED (alpha beta 10) of 69.93 (range: 48.00- 100.00) and mean BED (alpha beta 3) of 139.95 (range: 72.00- 216.67). Nodal locations treated included para-aortic (n= 7), pelvis (n= 5), iliacs (n= 4), and distant nodes (n= 3, supraclavicular, perirectal, periadrenal). Three pelvic and iliac lesions recurred locally at a median of 2.9 months (range 1.25-29.23) and 50% of patients developed local or distant recurrence. Local control at 3 months, 6 months, 1 year, and 2 years was 84.6%, 83.3%, 81.8%, and 70.0% respectively. Local recurrence free survival (LRFS) at 1 year and 2 years was 80.0%, 75.0% and overall survival (OS) was 54.5%, 44.4% respectively. Six patients developed grade 1 or 2 GI treatment related toxicity (Grade 1: 3, Grade 2: 3). 2 patients developed grade 1 GU toxicity. There were no GI grade 3-5 toxicities or GU grade 2-5 toxicities. Average Dmax to bladder was 14.42 Gy (median 18.62 Gy), range (0.6- 30.6). Average rectum Dmax was 15.75 Gy (range: 0.1- 34.2), median: 15.8 Gy. Median small bowel D2cc was 27 Gy (range: 10- 50.5) <h3>Conclusion</h3> Patients treated with proton SBRT had excellent rates of LC and LRFS and low rates of GI and GU toxicity. Dosimetric analysis also demonstrates low dose to nearby OARs. Based on these findings, Phase I/II trials evaluating dose escalation using proton techniques to further improve local control of isolated nodal recurrences should be considered.

Timing and necessity of staging imaging in clinical stage II cutaneous melanoma: Cost-effectiveness and clinical decision analysis

BackgroundPreoperative imaging in clinical stage II melanoma is not indicated per National Comprehensive Cancer Network (NCCN) guidelines but remains common in clinical practice. MethodsPatients presenting with cutaneous clinical stage II melanoma from 2007 to 2019 were retrospectively reviewed. A clinical decision analysis with cost data was designed to understand ideal practice patterns in managing stage II melanoma, with pre-versus selective post-operative imaging as the initial decision node. ResultsThere were 277 subjects included, and 143 underwent preoperative imaging (49.5%). This changed management (i.e. no surgery) in one patient (0.4%). Overall, 16 patients had additional findings on imaging (5.8%). Upfront surgery with selective postoperative imaging was a more cost-effective strategy than routine performance of preoperative imaging, with savings of $1677 per patient. ConclusionPreoperative imaging is a low yield, costly approach for patients with clinical stage II melanoma with minimal impact on the decision to proceed with surgical management.

Efficient Algorithms for Maximizing Group Influence in Social Networks

In social network applications, individual opinion is often influenced by groups, and most decisions usually reflect the majority's opinions. This imposes the group influence maximization (GIM) problem that selects <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> initial nodes, where each node belongs to multiple groups for a given social network and each group has a weight, to maximize the weight of the eventually activated groups. The GIM problem is apparently NP-hard, given the NP-hardness of the influence maximization (IM) problem that does not consider groups. Focusing on activating groups rather than individuals, this paper proposes the complementary maximum coverage (CMC) algorithm, which greedily and iteratively removes the node with the approximate least group influence until at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> nodes remain. Although the evaluation of the current group influence against each node is only approximate, it nevertheless ensures the success of activating an approximate maximum number of groups. Moreover, we also propose the improved reverse influence sampling (IRIS) algorithm through fine-tuning of the renowned reverse influence sampling algorithm for GIM. Finally, we carry out experiments to evaluate CMC and IRIS, demonstrating that they both outperform the baseline algorithms respective of their average number of activated groups under the independent cascade (IC) model.

Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model

The stability operation and real-time control of the integrated energy system with distributed energy resources determines the higher and higher requirements for the accuracy of solar photovoltaic (PV) output power prediction. This paper proposes an accurate PV power prediction interval approach based on frequency-domain decomposition and hybrid deep learning (DL) model. In the proposed approach, ensemble empirical mode decomposition (EEMD) is firstly used to decompose and reconstruct the original PV energy time-series data into high and low-frequency sub-series followed by the statistical feature extraction process. Furthermore, an improved long-short-term-memory network (LSTM) model with the designed hyperparameters based on Bayesian optimization (BO) is proposed to predict the sub-series with the different minute-hour-day intervals. Moreover, support vector regression (SVR) is utilized to analyze the initial time node and reduce the fluctuation error of the prediction value near zero. Finally, a comparative study with SVR, KNN, BPNN, GRU, Stacked-LSTM, LSTM, LSTM-SVR, and LSTM-SVR-BO models is constructed by using an actual dataset collected from Arizona, US. The simulation results on the datasets show the proposed prediction model outperforms the other 7 models for PV power forecasting in 1 day, 7 days, and 14 days ahead prediction with the different minute-hour-day intervals. Especially, in the seven days ahead prediction case, the proposed model's average RMSE and AbsDEV values are as low as 4.157 and 0.116, where the prediction accuracy and prediction stability are improved by about 15% on average compared to the other prediction models.

Sistem Pakar Untuk Mendiagnosa Penyakit Lupus dengan Metode Forward Chaining Menggunakan Web

Lupus disease can attack humans quickly. The initial symptoms that will arise are the same as common diseases such as high fever, prolonged thrush, and also hair will fall out and start to run out one by one until it will go bald, the form of red rashes that appear on the skin, excessive joint pain in the body, easily tired, weight loss, causing paralysis, there are many other symptoms. The lack of availability of a doctor and the limited knowledge of people in remote areas often make the initial diagnosis of the disease late, therefore the application of an expert system is considered very useful to help diagnose lupus early in view of the time and energy of an expert doctor. In the health sector there is an artificial intelligence called an expert system, which is a computer system that uses knowledge, facts and reasoning techniques in solving problems that can usually only be solved by an expert in their field. This study uses an expert system application method in making decisions using a forward chaining inference engine where the goal driven data will start a search on the initial node to the goal node until it gets results. From the diagnostic process, the system provides a selection of the symptoms experienced by the patient, resulting in CLE type lupus (Cutaneus Lupus Erythematosus)

Dynamic node influence tracking based influence maximization on dynamic social networks

Influence maximization is one of the popular problems in social network analysis. Its application includes viral marketing, epidemic control, and recommender systems. Most of the existing methods are applicable to static networks. However, many real-world networks are dynamic and they evolve with time. This paper studies influence maximization on dynamic social networks and proposes Dynamic Influence based Seed Selection (DYISSE) method. To find ‘k’ most influential nodes, the proposed method first estimates the influence of each node by introducing a Two-hop Triangular Influence (TTI) that measures the influence strength of each node by utilizing the property of triangles. Based on the TTI, a method named the Dynamic Tractable Set (D-TeSt) is proposed to track the changes in the influence of individual nodes when the topology of the network changes with time. The performance of the DYISSE method is analyzed under the LAIC model on two real-world and twelve synthetic dynamic networks. The results show that the proposed method performs better than the temporal versions of DegreeDiscount, MaxDegree, K-shell, Random, and Closeness centrality measures in extracting initial influential nodes.

Understanding decentralization of decision-making power in proof-of-stake blockchains: an agent-based simulation approach

ABSTRACT Blockchain systems allow for securely keeping shared records of transactions in a decentralised way. This is enabled by algorithms called consensus mechanisms. Proof-of-work is the most prominent consensus mechanism, but environmentally unsustainable. Here, we focus on proof-of-stake, its best-known alternative. Importantly, decentralised decision-making power is not an inherent feature of blockchain systems, but a technological possibility. Numerous security incidents illustrate that decentralised control cannot be taken for granted. We therefore study how key parameters affect the degree of decentralisation in proof-of-stake blockchain systems. Based on a real-world implementation of a proof-of-stake blockchain system, we conduct agent-based simulations to study how a range of parameters impact decentralisation. The results suggest that high numbers of initial potential validator nodes, large transactions, a high number of transactions, and a very high or very low positive validator network growth rate increase decentralisation. We find weak support for an impact of changes in transaction fees and initial stake distributions. Our study highlights how blockchain challenges our understanding of decentralisation in information systems research, and contributes to understanding the governance mechanisms that lead to decentralisation in proof-of-stake blockchain systems as well as to designing proof-of-stake blockchain systems that are prone to decentralisation and therefore more secure.

Trust Management in Growing Decentralized Networks

Abstract In a volunteer-based self-organizing network, such as a peer-to-peer network and a wireless ad-hoc network, trustworthiness of each participant node is crucial to assure information security and resilience of the whole network. We call this kind of network a “decentralized” network. In a decentralized network, mutual evaluation of trust among nodes are considered useful to manage trustworthiness in many studies. As far as we know, all the preceding related studies discussed trust management upon stable mature networks. However, most decentralized networks in reality begin with initial states comprised of a small number of nodes, and grow gradually with new-comer nodes joining in. This paper proposes a new trust management method for a growing decentralized network. The network grows from the initial stage where we assume all the initial nodes are trustworthy enough. This paper exhibits that our method extending GossipTrust brings better performance and better quality of trust evaluation than the original GossipTrust. Keywords: Trust, Reputation, P2P networks, Ad-hoc networks, Network growth.

Target Set Selection Parameterized by Vertex Cover and More

Diffusion is a natural phenomenon in many real-world networks. Spreading of ideas, rumors in an online social network; propagation of virus, malware in a computer network; spreading of diseases in a human contact network, etc. are some real-world examples of this. Diffusion often starts from a set of initial nodes known as seed nodes. A node can be in any one of the following two states: influenced (active) or not influenced (inactive). We assume that a node can change its state from inactive to active, however, not vice versa. Only the seed nodes are active initially and the information is dissipated from these seed nodes in discrete time steps. Each node v is associated with a threshold value τ(v) which is a positive integer. A node v will be influenced at time step \(t^{\prime }\), if there are at least τ(v) number of nodes in its neighborhood which have been activated on or before time step \(t^{\prime }-1\). The diffusion stops when no more node-activation is possible. Given a simple, undirected graph G with a threshold function \(\tau :V(G) \rightarrow \mathbb {N}\), the Target Set Selection (TSS) problem is about choosing a minimum cardinality set, say \(S \subseteq V(G)\), such that starting a diffusion process with S as its seed set will eventually result in activating all the nodes in G. For any non-negative integer i, we say a set \(T\subseteq V(G)\) is a degree-i modulator of G if the degree of any vertex in the graph G − T is at most i. Degree-0 modulators of a graph are precisely its vertex covers. Consider a graph G on n vertices and m edges. We have the following results on the TSS problem: It was shown by Nichterlein et al. (Soc. Netw. Anal. Min. 3(2), 233–256 10) that it is possible to compute an optimal-sized target set in \(\boldsymbol {O}(\boldsymbol {2}^{(\boldsymbol {2}^{t}+\boldsymbol {1})\boldsymbol {t}}\boldsymbol {\cdot m})\) time, where t denotes the cardinality of a minimum degree-0 modulator of G. We improve this result by designing an algorithm running in time \(\boldsymbol {2}^{\boldsymbol {O}(\boldsymbol {t}\log \boldsymbol {t})}\boldsymbol {n}\). We design a \(\boldsymbol {2}^{\boldsymbol {2}^{\boldsymbol {O}(\boldsymbol {t})}}\boldsymbol {n}^{\boldsymbol {O}(\boldsymbol {1})}\) time algorithm to compute an optimal target set for G, where t is the size of a minimum degree-1 modulator of G. We show that for a graph on n vertices of treewidth s, the TSS problem cannot be solved in \(\boldsymbol {f}(\boldsymbol {s})\boldsymbol {n}^{\boldsymbol {o}(\boldsymbol {\frac {s}{\log s}})}\) time unless the Exponential Time Hypothesis fails. This is an improvement over the previously known lower bound of \(\boldsymbol {f}(\boldsymbol {s})\boldsymbol {n}^{\boldsymbol {o}(\sqrt {\boldsymbol {s}})}\) due to Ben-Zwi et al. (Discret. Optim. 8(1), 87–96 16). In fact, we prove that same lower bound holds when parameterized by tree-depth or star-deletion number.

Attributed community search based on seed replacement and joint random walk

Community search enables personalized community discovery and has wide applications in real-life scenarios. Existing attributed community search algorithms use personalized information provided by attributes to locate desired community. Though achieved promising results, existing works suffer from two major limitations: (i) the precision of the algorithm decreases significantly when the seed comes from the boundary regions of the community. (ii) Most attributed community search methods mainly take the attribute information as edge weights to reveal semantic strength (e.g., attribute similarity, attribute distance, etc.), but largely ignore that attribute may serve as heterogeneous vertex. To make up for these deficiencies, in this paper, we propose a novel two-stage attributed community search method with seed replacement and joint random walk (SRRW). Specifically, in the seed replacement stage, we replace the initial query node with a core node; in the random walk stage, attributes are taken as heterogeneous nodes and the augmented graph is modeled based on the affiliation of the attributes via an overlapping clustering algorithm. And finally, a joint random walk is performed on the augmented graph to explore the desired local community. We conduct extensive experiments on both synthetic and real-world benchmarks, demonstrating its effectiveness for attributed community search.

Heterogeneous Graph Neural Network for Short Text Classification

Aiming at the sparsity of short text features, lack of context, and the inability of word embedding and external knowledge bases to supplement short text information, this paper proposes a text, word and POS tag-based graph convolutional network (TWPGCN) performs short text classification. This paper builds a T-W graph of text and words, a W-W graph of words and words, and a W-P graph of words and POS tags, and uses Graph Convolutional Network (GCN) to learn its feature and performs feature fusion. TWPGCN only focuses on the structural information of text graph, and does not require pre-training word embedding as initial node features, which improves classification accuracy, increases computational efficiency, and reduces computational difficulty. Experimental results show that TWPGCN outperforms state-of-the-art models on five publicly available benchmark datasets. The TWPGCN model is suitable for short text or ultra-short text, and the composition method in the model can also be extended to more fields.

An improved voterank algorithm to identifying a set of influential spreaders in complex networks

Identifying a set of critical nodes with high propagation in complex networks to achieve maximum influence is an important task in the field of complex network research, especially in the background of the current rapid global spread of COVID-19. In view of this, some scholars believe that nodes with high importance in the network have stronger propagation, and many classical methods are proposed to evaluate node importance. However, this approach makes it difficult to ensure that the selected spreaders are dispersed in the network, which greatly affects the propagation ability. The VoteRank algorithm uses a voting-based method to identify nodes with strong propagation in the network, but there are some deficiencies. Here, we solve this problem by proposing the DILVoteRank algorithm. The VoteRank algorithm cannot properly reflect the importance of nodes in the network topology. Based on this, we redefine the initial voting ability of nodes in the VoteRank algorithm and introduce the degree and importance of the line (DIL) ranking method to calculate the voting score so that the algorithm can better reflect the importance of nodes in the network structure. In addition, the weakening mechanism of the VoteRank algorithm only weakens the information of neighboring nodes of the selected nodes, which does not guarantee that the identified initial spreaders are sufficiently dispersed in the network. On this basis, we consider all the neighbors nodes of the node’s nearest and next nearest neighbors, so that the crucial spreaders identified by our algorithm are more widely distributed in the network with the same initial node ratio. In order to test the algorithm performance, we simulate the DILVoteRank algorithm with six other benchmark algorithms in 12 real-world network datasets based on two propagation dynamics model. The experimental results show that our algorithm identifies spreaders that achieve stronger propagation ability and propagation scale and with more stability compared to other benchmark algorithms.

Treatment outcomes of 156 patients with cervical esophageal cancers treated with definitive radiation therapy- A single-institution experience of a rare cancer.

Esophageal cancer is the most prevalent malignant tumor. The incidence of cervical esophageal cancer is low and there are insufficient data on the efficacy of radical radiotherapy. The purpose of this study was to clarify the efficacy with radical IFI radiotherapy, to analyze the pattern of initial lymph node metastasis and recurrence under the new lymph node zoning of esophageal cancer. We reviewed cervical esophageal cancer treated with radical radiotherapy. The inclusion criteria were diagnosis of esophageal cancer by pathology; receiving radical radiotherapy or chemoradiotherapy; tumor location in accordance with definition of cervical esophageal cancer. Three dimensional radiotherapy was used. The target area was IFI. 156 patients entered the final analysis. The proportion of no failure was 42.31%, local esophageal failure was 30.13%, in-field lymph node metastasis was 10.26%, out-field lymph node metastasis was 1.28% and distant organ metastasis was 23.72%, second primary tumor was 2.56%. The median OS and DFS was 49.0 months (35.27-62.73) and 31.0 months (14.47-47.53). The results of initial LN metastasis pattern analysis showed the supraclavicular and upper mediastinum were the main sites of cervical esophageal cancer metastasis. In patients with recurrent LN, the results showed that the cervical, supraclavicle, upper mediastinum and abdomen were the main sites of recurrence. Our study is a retrospective study of a large sample of radical radiotherapy for cervical esophageal cancer. Failure in irradiation field is the main failure pattern. Concurrent radiotherapy and chemotherapy under IFI radiation is a considerable treatment option for cervical esophageal cancer.

Adaptive control arc length-based time grid refinement control parameterisation method for unmanned hypersonic vehicle reentry trajectory optimisation

An adaptive control arc length-based trajectory optimisation approach, which combines the control vector parameterisation (CVP) method with Gauss time grid distribution and adaptive grid refinement strategy, is proposed to further improve the downrange of unmanned hypersonic vehicles. Firstly, the Gauss time grid strategy is introduced to obtain a nonuniform Gauss time grid to calculate the original control profiles. By employing the curve integral formula to analyse the control arc length, an adaptive time grid refinement concept is proposed and the refinement process is derived in detail. The initial time grid nodes are then adaptively subdivided and combined. Finally, the control arc length-based nonuniform time grid refinement optimisation method is implemented. Numerical tests are carried out on common aero hypersonic vehicles and comparisons are made with other CVP methods. The test results reveal that the proposed approach can efficiently improve the downrange and decrease the path constraint oscillation.