Dominant Node Research Articles

Collaborative optimization allocation of VDAPFs and SVGs for simultaneous mitigation of voltage harmonic and deviation in distribution networks

This paper proposes a collaborative optimization allocation strategy for voltage-detection active power filters (VDAPFs) with multiple functions and static var generators (SVGs) to improve voltage harmonic (VH) and voltage deviation (VD) of distribution networks (DNs). The integrated voltage sensitivity is defined considering VH and VD simultaneously. According to the local characteristic of power quality (PQ), a partition algorithm based on integrated voltage sensitivity is developed to divide a network into several regions. Two methods based on single and integrated sensitivity are presented to determine dominant nodes as candidates of VDAPFs and SVGs in each region. The corresponding allocations of VDAPFs and SVGs are respectively provided considering the two methods. The sites and sizes of VDAPFs and SVGs are optimized through minimizing the total cost including investment and operation costs by improved particle swarm optimization (PSO) algorithm. An integrated evaluation index based on economy and PQ disturbance mitigation effect is defined to evaluate the performance of the two allocations, thereby determining an optimal allocation. The feasibility and effectiveness of the proposed strategy are verified implemented on IEEE 33-bus system and IEEE 69-bus system by comparing the two allocations with each other and with the conventional allocation strategy.

Identifying early defects of wind turbine based on SCADA data and dynamical network marker

Defects Identification is of great significance to prevent wind turbines (WTs) accidents and improve its operation reliability, and it keeps challenging due to the complex relationship between internal faults and external observed data. A new method to identify early defects of WTs is presented based on Dynamical Network Marker (DNM) by adopting only the data of supervisory control and data acquisition (SCADA). In the presented method, WT is mapped into a multi-node complex network according to the corresponding relationship between the internal structure topology of WT and the monitoring variables of its SCADA system. Then the dominant nodes in the network under different states are screened out through the correlation and cross-correlation analysis of de-nosed SCADA monitoring data series and prediction data series to form a key subnetwork, and the dynamical network marker (DNM) is constructed as warning signal of defects of WT. The proposed method is tested with the SCADA data of a WT with known faults. The results illustrate that the proposed method can not only give an early warning signal when WT under defect state but also further determine the defect location. Moreever, the proposed method only needs the SCADA monitoring data of WT itself, which is convenient and easy to be popularized.

CLINICAL AND HISTOLOGIC FEATURES OF UTERINE MYOMA IN REPRODUCTIVE-AGE WOMEN UNDER THE TREATMENT BY SELECTIVE PROGESTERONE RECEPTOR MODULATORS

Aim of the study was to investigate the clinical features of reproductive age women with uterine body myoma and the production of vascular endothelial growth factor (VEGF) in the dominant myoma node and perifocal myometrium under the preoperative use of ulipristal acetate (UA). Material and methods. 140 samples of 35 women surgical material were studied, VEGF content in the incubation medium of samples of myoma and myometrium was measured using enzymelinked immunosorbent assay. Results and discussion. The treatment by UA before myoma surgery according to the standard regimen per 5 mg daily for 13 weeks deals with significantly reduced production of VEGF in the dominant myoma node compared to perifocal myometrium ( p < 0.001), which is similar to patients of the control group. However, when comparing tumor nodes, a significantly lower content of VEGF was revealed in the UA-group ( p = 0.026); unlike the perifocal myometrium samples. Conclusion . The use of a selective modulator of progesterone receptors downregulates the production of VEGF by the cells of the myoma nodes and doesn’t impact on the perifocal myometrium.

Identification of Influential Spreaders in Social Networks using Improved Hybrid Rank Method

Abstract Online Social Networks provide us a gateway towards valuable information regarding personal preferences, interests, and connections. Ranking nodes by centrality measures help us to identify some of the most critical and influential nodes in a network who play a crucial role in the information spreading process and influence maximization. Traditional centrality measures have certain restraints and do not provide optimal results single-handedly. With the advances in research, leading to the formulation of the concept of hybrid centrality, the detection of the most influential spreaders in a network has shown a large scale of improvement. Some of the real-world phenomena such as viral marketing, rumor spreading, etc. can be addressed by leveraging the fact that only the dominant and authoritative nodes play a crucial role in propagating information about a new product or idea and those nodes can also help in blocking false information or rumors from reaching the other parts of the network. In this paper, we present the Improved Hybrid Rank algorithm, which combines two centralities, namely, the Extended Neighborhood Coreness centrality and the H-Index centrality. The results obtained by simulating our proposed method using the SIR (Susceptible-Infected-Recovered) model on different un-directed and directed real-world networks show that the ranking of nodes and the choice of influential spreaders, as proposed by our algorithm outperforms many other classical methods.

Plithogenic Cognitive Maps in Decision Making

Plithogenic sets introduced by Smarandache (2018) have disclosed new research vistas and this paper introduces the novel concept of plithogenic cognitive maps (PCM) and its applications in decision making. The new approach of defining instantaneous state neutrosophic vector with the confinement of indeterminacy to (0,1] is proposed to quantify the degree of indeterminacy. The resultant vector is obtained by applying instantaneous state vector through the connection matrix together with plithogenic operators comprising the contradiction degrees. The connection matrix is represented as fuzzy matrix and neutrosophic matrix and the resultant vector is determined by applying plithogenic fuzzy operators and plithogenic neutrosophic operators respectively. The obtained results are highly feasible in making the decision as it incorporates the contradiction degree of the conceptual nodes with respect to the dominant node. This research work will certainly pave the way for developing new approaches in decision making using PCM.

CBMT-28. FATTY ACID OXIDATION PROVIDES METABOLIC PLASTICITY TO MAINTAIN GROWTH IN THE DYNAMIC MICROENVIRONMENT OF GLIOBLASTOMA

Abstract Glioblastoma represents an aggressive, primary brain tumor with limited treatment options. Despite advances in molecularly characterizing glioblastoma, metabolic alterations driving its aggressive phenotype are only beginning to be recognized. Integrative cross-platform analyses coupling global metabolomic and gene expression profiling identified alterations in fatty acid β-oxidation (FAO) as a key metabolic node differentiating glioblastoma from low-grade astrocytoma, which was defined by an accumulation of acylcarnitines. Metabolic heterogeneity was observed within glioblastoma that could further define tumors as FAO ‘high’ and ‘low’, which were enriched with mesenchymal and proneural subtypes of glioblastoma, respectively. These findings were metabolomically and functionally recapitulated in molecular subtype-specific preclinical models, with the majority of baseline mitochondrial oxygen consumption being a result of enhanced FAO in these cells. The biologic consequence of enhanced FAO in glioblastoma is directly dependent upon tumor microenvironment. FAO serves as a metabolic cue to drive proliferation in a β-HB/GPR109A/cAMP-dependent autocrine manner in nutrient favorable conditions while providing an efficient, alternate source of ATP only in nutrient unfavorable conditions. Accordingly, inhibiting FAO alone in glioblastoma cells with etomoxir only led to modest anti-proliferative activity and minimal cytotoxicity. However, rational combinatorial strategies designed to target the dynamic roles FAO plays in gliomagenesis resulted in metabolic synthetic lethality in glioblastoma. Specifically, dual targeting of FAO (etomoxir) and glycolysis (2DG) resulted in robust energetic stress, necroptosis mediated cell death, and a significant improvement in survival in an orthotopic glioblastoma mouse model. In summary, we identified FAO as a dominant metabolic node in glioblastoma that provides metabolic plasticity, allowing these cells to adapt to their dynamic microenvironment. Combinatorial strategies designed to target these diverse roles FAO plays in gliomagenesis offers therapeutic potential in glioblastoma.

Improving Energy Efficiency in Wireless Sensor Networks through Virtual-Backbone–Based Topology Control Mechanism

Abstract This article presents a design of a virtual-backbone–based topology control scheme that builds the virtual backbone with the help of localized information. Initially, the dominating set is constructed using spanning tree application. The resultant dominating set spanning tree is further optimized to remove dominant act nodes because, originally, they are the regular nodes, and it forms a small-sized backbone with selected sufficient energy capacity nodes. Then the 3-hop message forward algorithm is used to identify the relay nodes. The roles of the relay nodes are to make the dominating set as a connected dominating set (CDS). Finally, a minimal CDS is formed by a heuristic approach to remove redundant sensors from the CDS.

Predicting sectoral electricity consumption based on complex network analysis

High-frequency and unit-level consumption data collected by smart meters makes accurate and short-term predictions of sectoral electricity demand possible. To facilitate electricity market pricing, load management and demand response, models handling such high-dimensional data-sets are expected to realize effective variable selection, accurate prediction and, in the meantime, retain the economic mechanisms as much as possible. This paper attempts to propose a complex network based on a variable selection model that retains the causality relationships among the most relevant sectors and can achieve prediction accuracy that is comparable to other data-driven models. A dataset containing 266,000 industrial and commercial firms in Shanghai is employed to develop a complex network relying on Granger causality and correlation coefficients. Dominant nodes are selected based on a Planar Maximally Filtered Graph algorithm and then serve as explanatory variables in the linear regression model. Further comparison with LASSO, PCA and Ridge regression shows that this model can successfully realize dimension reduction but maintain significant economic mechanisms, and achieving unbiased estimation and acceptable accuracy.

Counter-Community as Moral Community: The Church’s Contribution to Public Civility

Indonesia is a home of various religions and traditional beliefs. However, from the time before the fall of the New Order in 1998 up to now the coexistence of followers of different religions cannot be said to be without problems. The 2004 Indonesian Bishops’ Conference’s pastoral notes reflect the worry that the peaceful coexistence among different religious followers is failing. Nevertheless, the document’s dominant node is expressed in positive attitude and hope. The Indonesian Bishops’ Conference encourages all Christian communities to develop themselves into ‘counter-communities’. This article is aimed at discovering the relevance of the idea ‘counter-communities’ to the problems of coexistence of different religious followers in the Post-New-Order era, which now has been marked with prejudice and mistrust.

Abstract 5280: Branched chain amino acids represent indispensible metabolic substrates in glioblastoma

Abstract Glioblastoma (GBM) is an aggressive brain tumor with limited treatment options. Considerable advances have been made in understanding the genetic alterations driving these tumors, however, the metabolic alterations and mechanisms underlying these aggressive phenotypes remain unclear. We performed global metabolomic profiling of patient derived low-grade astrocytoma (LGA, n= 28) and GBM (n=80). Hierarchical clustering (HC) identified amino acid (AA) metabolism as a dominant metabolic node in GBM compared to LGA. Considerable inter-tumoral heterogeneity of AA metabolism was observed in GBM, with HC identifying two clearly distinct metabolic subtypes consisting of increased and decreased concentrations of AA and their metabolic intermediates. VIP analysis identified the accumulation of branched chain amino acids (BCAA)- leucine, valine and isoleucine as the top ranked metabolites differentiating these subtypes. Based on this, GBMs were classified as BCAA-high and BCAA-low. BCAA-high GBM was enriched with mesenchymal and classical subtypes while BCAA-low GBM was enriched with proneural and neural subtypes. Integrative analysis coupling these metabolic signatures with gene expression profiling performed on matched tissue demonstrated upregulation of BCAA metabolism-associated genes in BCAA-high GBM, along with global transcriptional programs designed to activate cell cycle progression. When extended into molecular subtype specific preclinical models, we demonstrated that BCAA were indispensible for survival of mesenchymal GBM cells, which was independent of the presence or absence of other non-essential and essential amino acids. Further, these cells were able to maintain long-term survival in a dormant state when grown in media devoid of all essential and non-essential AA other than BCAA and glutamine. Knockdown of key enzymes in BCAA metabolism led to robust cytotoxicity in this model, underscoring the importance of BCAA in GBM survival. Ongoing studies are designed to determine the biologic consequence and intermediary metabolism of BCAA in GBM. Collectively these results identify BCAA metabolism as a therapeutic target in GBM. Citation Format: Antony H. Prabhu, Shiva Kant, Pravin Kesarwani, Prakash Chinnaiyan. Branched chain amino acids represent indispensible metabolic substrates in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5280.

A balanced energy efficient virtual backbone construction algorithm in wireless sensor networks

A steady increase in the number of Wireless Sensor Networks (WSN) applications in day-to-day life lead to the design of new algorithms and protocols to solve their various issues such as energy efficiency, topology control and data gathering. Energy efficiency can be achieved by focusing on the communication part, since the communication cost is higher than that of the computation. The use of Connected Dominating Set (CDS) as the virtual backbone for data transmission towards the Base Station improves the network lifetime. Therefore, a Balanced Energy Efficient Virtual Backbone Construction (BEE-VBC) algorithm in WSN is proposed in this paper using the well-known heuristic Genetic Algorithm (GA). This work emphasizes on the design of fitness function using multiple factors such as energy, coverage of neighbors, reliability, data rate, and traffic deviation. The CDS construction using the maximum fitness nodes, provides network longevity. This ensures a steady connectivity among the dominator nodes and path towards the BS, thus improving the quality of the solution. The simulation experiments have been carried out and compared with a few CDS-based and clustering-based algorithms to show the performance improvement achieved in the proposed work.

Efficient time reducing and energy saving routing algorithm for wireless sensor network

The research article presents a novel energy saving routing algorithm for wireless sensor networks (WSNs) to meet the demand of present research trend. In contrast to conventional methods, a new concept of dominant node has been introduced in this paper. Unlike the conventional algorithms, the proposed method can be applied to both homogeneous and heterogeneous networks. The proposed method focuses on the proper energy distribution among the sensor nodes so that it can offer an energy efficient solution while maintaining improved network lifetime and throughput. The proposed idea has been validated through simulation results. Moreover, the supremacy of the proposed method has been established over existing ones.

Macroscale intrinsic network architecture of the hypothalamus

Control of multiple life-critical physiological and behavioral functions requires the hypothalamus. Here, we provide a comprehensive description and rigorous analysis of mammalian intrahypothalamic network architecture. To achieve this at the gray matter region (macroscale) level, macroscale connection (macroconnection) data for the rat hypothalamus were extracted from the primary literature. The dataset indicated the existence of 7,982 (of 16,770 possible) intrahypothalamic macroconnections. Network analysis revealed that the intrahypothalamic macroconnection network (its macroscale subconnectome) is divided into two identical top-level subsystems (or subnetworks), each composed of two nested second-level subsystems. At the top-level, this suggests a deeply integrated network; however, regional grouping of the two second-level subsystems suggested a partial separation between control of physiological functions and behavioral functions. Furthermore, inclusion of four candidate hubs (dominant network nodes) in the second-level subsystem that is associated prominently with physiological control suggests network primacy with respect to this function. In addition, comparison of network analysis with expression of gene markers associated with inhibitory (GAD65) and excitatory (VGLUT2) neurotransmission revealed a significant positive correlation between measures of network centrality (dominance) and the inhibitory marker. We discuss these results in relation to previous understandings of hypothalamic organization and provide, and selectively interrogate, an updated hypothalamus structure-function network model to encourage future hypothesis-driven investigations of identified hypothalamic subsystems.

Design and Evaluation of Algorithms for Energy Efficient and Complete Determination of Critical Nodes for Wireless Sensor Network Reliability

A critical node (cut vertex or articulation point) in wireless sensor networks, is a node which its failure breaks the connectivity of the network. Therefore, it is crucial that critical nodes be detected and treated with caution. This paper provides two localized distributed algorithms for determining the states of nodes (critical or noncritical). The first proposed algorithm identifies most of the critical and noncritical dominator nodes from two-hop local subgraph and connected dominating set (CDS) information that limits the computational complexity to $O(\Delta ^2)$ and bit complexity to $O(c\text{log}_2n)$ where $\Delta$ is the maximum node degree, $c$ is the critical node count, and $n$ is the node count. The testbed experiments and simulation results show that this algorithm detects up to 93% of critical nodes and achieves up to 91% of state determination with low energy consumption. The second proposed algorithm, which is based on the first one, finds the states of all nodes by running a limited distributed depth-first search algorithm in unrecognized parts of the network without traversing the whole network. Comprehensive testbed experiments and simulation results reveal that, in the presence of a CDS, this algorithm finds all critical nodes with lower energy consumption than all existing algorithms.

Integrative cross-platform analyses identify enhanced heterotrophy as a metabolic hallmark in glioblastoma.

Although considerable progress has been made in understanding molecular alterations driving gliomagenesis, the diverse metabolic programs contributing to the aggressive phenotype of glioblastoma remain unclear. The aim of this study was to define and provide molecular context to metabolic reprogramming driving gliomagenesis. Integrative cross-platform analyses coupling global metabolomic profiling with genomics in patient-derived glioma (low-grade astrocytoma [LGA; n = 28] and glioblastoma [n = 80]) were performed. Identified programs were then metabolomically, genomically, and functionally evaluated in preclinical models. Clear metabolic programs were identified differentiating LGA from glioblastoma, with aberrant lipid, peptide, and amino acid metabolism representing dominant metabolic nodes associated with malignant transformation. Although the metabolomic profiles of glioblastoma and LGA appeared mutually exclusive, considerable metabolic heterogeneity was observed in glioblastoma. Surprisingly, integrative analyses demonstrated that O6-methylguanine-DNA methyltransferase methylation and isocitrate dehydrogenase mutation status were equally distributed among glioblastoma metabolic profiles. Transcriptional subtypes, on the other hand, tightly clustered by their metabolomic signature, with proneural and mesenchymal tumor profiles being mutually exclusive. Integrating these metabolic phenotypes with gene expression analyses uncovered tightly orchestrated and highly redundant transcriptional programs designed to support the observed metabolic programs by actively importing these biochemical substrates from the microenvironment, contributing to a state of enhanced metabolic heterotrophy. These findings were metabolomically, genomically, and functionally recapitulated in preclinical models. Despite disparate molecular pathways driving the progression of glioblastoma, metabolic programs designed to maintain its aggressive phenotype remain conserved. This contributes to a state of enhanced metabolic heterotrophy supporting survival in diverse microenvironments implicit in this malignancy.

Energy-Aware Control of Data Compression and Sensing Rate for Wireless Rechargeable Sensor Networks.

Wireless rechargeable sensor nodes can collect additional data, which leads to an increase in the precision of data analysis, when enough harvested energy is acquired. However, because such nodes increase the amount of sensory data, some nodes (especially near the sink) may blackout because more transmitted data can make relaying nodes expend more energy. In this paper, we propose an energy-aware control scheme of data compression and sensing rate to maximize the amount of data collected at the sink, while minimizing the blackout time. In this scheme, each dominant node determines the data quota that all its descendant nodes can transmit during the next period, which operates with an efficient energy allocation scheme. Then, the node receiving the quota selects an appropriate data compression algorithm and sensing rate according to both its quota and allocated energy during the next period, so as not to exhaust the energy of nodes near the sink. Experimental results verify that the proposed scheme collects more data than other schemes, while suppressing the blackout of nodes. We also found that it adapts better to changes in node density and harvesting environments.

Transportation network analysis in Nepal: a step toward critical infrastructure protection

Transportation infrastructure is vital for a nation to function smoothly. However, transportations systems are vulnerable to both natural and man-made hazards and breakdowns can have severe consequences. Therefore, it is important that they are protected and resilient against various types of harmful events. Using a graph-theoretic/social network approach, this study finds that the road and aviation networks of Nepal are different in terms of topology and the influence of nodes vary substantially between and across these networks. While Nepal’s strategic road network is generally robust and the influential nodes are well distributed spatially, the aviation network is less robust and there exists a single dominant node. The enactment of two national policy actions and the adoption of a new disaster response framework indicate that the Government of Nepal is concerned about the national consequences of calamities on these networks as well as responses in the aftermath of a disastrous event. However, the Government lacks a clear plan for protecting these critical infrastructures or limiting the consequential effects. This study provides some first step guidance for the Government of Nepal in developing a prioritization in the transportation element of a critical infrastructure protection plan intended to ensure resiliency against disruption and failure.

Construction Low Complexity and Low Delay CDS for Big Data Code Dissemination

The diffusion of codes is an important processing technology for big data networks. In previous scheme, data analysis was conducted for small samples of big data and complex problems that cannot be processed by big data technology. Due to the limited capacity of intelligence device, a better method is to select a set of nodes (intelligence device) to form a connected dominating set (CDS) to save energy, and constructing CDS is proved to be a complete NP problem. However, it is a challenge to reduce the communication delay and complexity for urgent data transmission in big data. In this paper, an appropriate duty cycle control (ADCC) scheme is proposed to reduce communication delay and complexity while improving energy efficient in CDS-based WSNs. In ADCC scheme, the method for constructing CDS is proposed at lower complexity. Nodes in CDS are selected according to the degree of nodes. Then, duty cycle of dominator nodes in CDS is higher than that of dominated nodes, so the communication delay in the proposed scheme is far less than that of previous scheme. The duty cycle of dominated nodes is small to save energy. This is because the number of dominator nodes in CDS is far less than the number of dominated nodes whose duty cycle is small; thus, the total energy consumption of the network is less than that of the previous scheme. As a result, the performance of energy consumption and communication delay and complex have been improved. Its complexity O∑i=0vm−i+2m−2v−s is reduced a lot for big data. The theoretical analysis shows that compared to the previous scheme, the transmission delay can be reduced 25–92% and the energy efficiency is improved by about 80% while retaining network lifetime.

Gene expression profiles of glucose toxicity‐exposed islet microvascular endothelial cells

Islet microcirculation is mainly composed by IMECs. The aim of the study was to investigate the differences in gene expression profiles of IMECs upon glucose toxicity exposure and insulin treatment. IMECs were treated with 5.6mmolL-1 glucose, 35mmolL-1 glucose, and 35mmolL-1 glucose plus 10-8 molL-1 insulin, respectively. Gene expression profiles were determined by microarray and verified by qPCR. GO terms and KEGG analysis were performed to assess the potential roles of differentially expressed genes. The interaction and expression tendency of differentially expressed genes were analyzed by Path-Net algorithm. Compared with glucose toxicity-exposed IMECs, 1574 mRNAs in control group and 2870 mRNAs in insulin-treated IMECs were identified with differential expression, respectively. GO and KEGG pathway analysis revealed that these genes conferred roles in regulation of apoptosis, proliferation, migration, adhesion, and metabolic process etc. Additionally, MAPK signaling pathway and apoptosis were the dominant nodes in Path-Net. IMECs survival and function pathways were significantly changed, and the expression tendency of genes from euglycemia and glucose toxicity exposure to insulin treatment was revealed and enriched in 7 patterns. Our study provides a microcirculatory framework for gene expression profiles of glucose toxicity-exposed IMECs.

THE EFFECT OF TREATMENT WITH MIFEPRISTONE ON THE QUALITY OF LIFE OF PATIENTS WITH UTERINE MYOMA

Purpose of the study: evaluation of the quality of life of patients with symptomatic uterine myoma up to 12 weeks of gestation against a background of mifepristone 50 mg daily continuously for 3 months.Material and methods. The prospective study involved 141 patients with uterine myoma who received treatment with mifepristone (Gynestril®) 50 mg continuously for 3 months. In order to evaluate changes in the volume of uterine bleeding, each patient kept a diary where she registered the number of sanitary napkins used. Sonographic examination was used to evaluate the size of the uterus and fibroids. The Visual Analogue Scale (10-point VAS scale) was used to assess the pain intensity. Quality of life of patients and their satisfaction with the therapy was also evaluated by a 10-point VAS scale. The complaints, physical examination data and results of sonographic studies were recorded before and immediately after the end of treatment (1st and 2nd visits).Study results. Drug therapy with Gynestril® (mifepristone 50 mg) for patients with uterine myoma for 3 months helped to obtain a statistically significant (p &lt; 0.001) improvement across all the evaluated parameters: in a large majority of patients, control of menstrual blood loss was achieved (decreased duration and volume of bleeding), while 85 patients (60.3%) achieved reversible drug-induced amenorrhea, and 43 patients (30.5%) - oligomenorrhea; a large majority of patients achieved reduction of pain severity according to VAS, while in 118 (83.7%) patients the parameter was estimated as “slight pain/no pain” after the end of treatment. According to the ultrasound results, a statistically significant decrease was achieved in the number of displayable fibroids (from 2.1 to 1.9 at an average), the volume of the dominant myoma node (on average by 65% from 37.34 to 13.27 cm3) and of the uterus (an average of 35% from 182.71 to 118.09 cm3). In addition, a vast majority of patients - 99.9% - evaluated the quality of life and satisfaction with treatment as high.Conclusions. Therefore, the results of the study demonstrate that Gynestril® is highly effective, safe and promising in the pharmaceutical treatment of uterine fibroids.