Network Analysis Tools Research Articles

Distribution System State Estimation Using PV Separation Strategy in LV Feeders With High Levels of Unmonitored PV Generation

Distribution system state estimation (DSSE) is a critical analysis tool for active distribution networks (DNs). Unlike weighted least squares techniques, which are static DSSE methods, the augmented complex Kalman filter (ACKF) is a novel technique that considers the system’s dynamic behavior. Currently, most DNs integrate a large number of unmonitored residential photovoltaic (PV) generations. Existing unmeasured PV sources violate the white noise assumption in Kalman and least-squares-based estimators, causing the estimator to be biased. Because the one-step difference of aggregated customer demand is characterized as white noise, the suggested PV estimation technique based on the differencing strategy is used to decouple PV from the measured load. Using the specified contribution factors, the new online pseudo current injections are generated. In addition, the estimator’s accuracy is improved by using a new PV-scaling-aided ACKF approach based on the PV separation strategy. For validation purposes, this method is applied to real DN case studies. This study also makes use of an actual dataset to illustrate the efficacy of the proposed technique. The proposed technique outperforms the existing snapshot and dynamic DSSE techniques, and significant improvements are achieved in terms of accuracy and computational cost.

Big data networks: Dynamic Time Warping as a statistical tool for network analysis using Ecological Momentary Assessment data

IntroductionIn recent research, psychological disorders have been increasingly defined as complex dynamic systems in which symptoms are interconnected and influence each other, thereby forming symptom networks. This paradigm shift calls for the analysis and interpretation of relationships between symptoms that are complex, potentially non-linear, and dynamic. Dynamic Time Warping (DTW) is used to measure similarity in temporal sequences, and has recently been found effective in modelling psychopathology symptom networks.ObjectivesWe aim to demonstrate that DTW could also be used to model the network structure in Ecological Momentary Assessment (EMA) data.Methods355 participants of the Netherlands Study of Depression and Anxiety (NESDA), of which 100 with and 255 without current disorder, completed EMA assessments of 20 symptoms (e.g., feeling sad, tired, satisfied) five times a day for two weeks. DTW analysis was performed on the group level, comparing participants suffering from mood disorders to healthy controls. DTW distances were visualized as an undirected symptom network, in which we adjusted for the average symptom severity per item per person.ResultsDTW analysis of close to half a million symptom scores yielded six symptom dimensions based on their aggregated similarity of changes over time within the participants. Surprisingly, negative affect symptom networks were found to be less strongly connected in those currently suffering from mood disorders than in controls, whereas the network density of (reverse-coded) positive affect symptoms was more closely connected in this group. This is contrary to the results of previous studies, where negative affect-related symptom networks of those with mood disorders were found to be more strongly interconnected.ConclusionsDTW is a promising new technique for analyzing EMA data and modeling dynamic symptom networks at both the individual and group levels. Using EMA data, symptom networks and dimensions can be modeled with great structural and temporal detail. Incorporating the temporal symptom dynamics may highlight the importance of the independent trajectories of negative mood symptoms.Disclosure of InterestNone Declared

Snout: A Middleware Platform for Software-Defined Radios

The plethora of Internet of Things (IoT) protocols and the upcoming availability of new spectrum bands for wirelessly connected devices have made software-defined radio (SDR) technology increasingly useful to interact with radio-based communication. While SDR-based tools have grown in popularity in recent years due to their flexibility and adaptability towards new protocols, SDR software interfaces remain highly complex and technical, and inherently require specialized skillsets in digital signal processing (DSP) to operate. To address this problem, we present Snout, an SDR middleware platform that encapsulates and abstracts much of the current complexity in SDR toolchains. This allows SDR developers to create wireless networking applications usable by a wide range of users. For instance, network security professionals can monitor the IoT landscape across multiple protocols without needing to interact with the underlying software-defined DSP. Snout implements interfaces with common network analysis tools to allow for integration with traditional network security solutions, facilitating use cases such as traffic analysis or rogue device detection. Its software architecture enables scalability in terms of protocols and processing by modularizing the signal processing pipeline. To demonstrate Snout’s capabilities, we show how it can encapsulate GNU Radio flowgraphs, facilitate simultaneous multi-protocol scanning, and convert existing SDR-based protocol implementations into fully contained applications. We further demonstrate how Snout can handle GNU Radio flowgraphs with other signal processing software simultaneously. Through extensive experiments, we demonstrate that Snout incurs limited CPU performance overhead below 4% and a memory footprint below 100MB, and handles large amounts of events with sub-millisecond latency.

Reverse predictive analysis of Rhizoma Pinelliae and Rhizoma Coptidis on differential miRNA target genes in lung adenocarcinoma.

To use bioinformatics and network analysis to reveal the mechanism of "Rhizoma Pinelliae-Rhizoma Coptidis" herb pair in the treatment of lung adenocarcinoma. The target and pathway of "Rhizoma Pinelliae-Rhizoma Coptidis" herb pair in the treatment of lung adenocarcinoma were explored by online databases and network analysis tools, and the potential biomarkers of "Rhizoma Pinelliae-Rhizoma Coptidis" herb pair in the treatment of lung adenocarcinoma were predicted in reverse. A total of 59 traditional Chinese medicine compounds and 510 drug targets were screened in this study. A total of 25 micro-RNAs and 15,323 disease targets were obtained through GEO2R software analysis. In the end, 294 therapeutic targets and 47 core targets were obtained. A total of 186 gene ontology enrichment assays were obtained, and core therapeutic targets play multiple roles in biological processes, molecular functions, and cellular composition. Kyoto encyclopedia of genes and genomes pathway enrichment analysis showed that the core targets were mainly enriched in cancer-related pathways, immune-related pathways, endocrine-related pathways, etc, among which the non-small cell lung cancer pathway was the most significant core pathway. Molecular docking shows that the compound and the target have good binding ability. "Rhizoma Pinelliae-Rhizoma Coptidis" herb pair plays a mechanism of action in the treatment of lung adenocarcinoma through multiple targets and pathways. miR-5703, miR-3125, miR-652-5P, and miR-513c-5p may be new biomarkers for the treatment of lung adenocarcinoma.

Computational analysis of interactions between AMPA receptor and con-ikot-ikot conotoxin.

Con-ikot-ikot (CII) is a toxin found in the venom of a marine snail Conus striatus, and the only conotoxin known so far to specifically target AMPA receptors (AMPAR) - a subtype of ionotropic glutamate receptors which are important for synaptic transmission and plasticity. It is small in size (∼20 kDa) and has a unique binding site, nestled between the layers of amino-terminal domains (ATDs) and ligand-binding domains (LBDs) of the receptor, which constitute its extracellular portion. Conotoxins and other animal-derived toxins have an established role as important molecular tools in studying various ion channels and some have already been repurposed as therapeutics, therefore we are interested in using CII to study AMPA receptors. In order to gain a better understanding of the interactions between con-ikot-ikot and AMPA receptors, we have performed all-atom molecular dynamics (MD) simulations of a complex of AMPAR extracellular domains (ATDs and LBDs) with glutamate and CII. Network analysis of the resulting MD simulation trajectories, performed using a residue interaction network (RIN)-based tool we are developing, recapitulates previously identified key residue interactions with ligand-binding domains (LBDs) of AMPARs, reveals novel interactions with the LBD layer and suggests interactions between CII and AMPAR amino-terminal domains (ATDs), making CII potentially the only known ligand to interact with AMPAR ATDs. Our new network analysis tool will allow the user to visualise and explore residue interaction networks of single frames, entire trajectories, or sets of trajectories. It will not only be a tool suitable for our needs in investigating the AMPAR/CII complex, but will also act as a general exploratory, hypothesis-generating tool that will be of interest for anyone looking to understand dynamic networks of protein-protein interactions.

Tecnologias da informação para a luta contra a corrupção: análise da contratação pública costarriquenha

Abstract Corruption has disastrous consequences for the well-being of societies. In particular, public procurement is a highly vulnerable factor due to the high value of its transactions. This research analyzes the role that information technologies can play in the fight against corruption. We developed a case based on open data on public procurement in Costa Rica to identify the main concepts associated with the underlying information system. The case shows the potential of technological tools for social network analysis to counter the complexity of corruption networks. It also shows the need for a rich ecosystem of anti-corruption data - preferably in an open format.

Enhance work for java based network analyzer tool used to analyze network simulator files

<span lang="EN-US">The network performance measurement is important in computer networks, and performance measurement may not be effective for installation in peripheral devices resulting in the replacement of those devices and thus increasing cost. In light of this, it is better to have a simulation of the network to see its performance rather than the actual design. NS-2 is one of the most popular and widely used open-source network simulators in many organizations, which generates trace files during the simulation experience. The trace file contains all network events that can be used to calculate performance. Thus, NS-2 does not offer any visualization options for analyzing simulation results (trace files), which is the fundamental problem of trace file parsing difficulty. This paper provides a graphical user interface tool that enables researchers to quickly and efficiently analyze and visualize NS-2 trace files. This tool is a development of the JDNA tool, as it could not analyze more than one trace file at a time. In addition, this work can be a useful guide for network researchers or other programmers to analyze their networks and understand how to calculate network performance metrics.</span>

Improving the Social Connectivity in Engineering Educational Networks and Implications

Introduction: It is theorized that improving the connectivity in engineering educational networks by strengthening the student–student and instructor–student relationships enhances the spread of knowledge, and results in an improved objectively assessed educational outcomes. Objectives: In this study, a structured semester-long educational experiment is conducted to strengthen connectivity and social relationships among engineering students in three undergraduate courses. Methods: Students are divided into mentoring groups, each associated with a homework assignment. The assignment is then solved for the mentor students, whereby nonmentor (mentee) students should discuss with at least one mentor student. The students then report their friendship level with each classmate on a scale from 0 to 4 with each submitted homework. Furthermore, the mentor students report the number of interactions with nonmentor students and the duration of each interaction. Results: The collected data points are analyzed using social network analysis tools, where an impressive improvement in the number and quality of relationships is achieved. Compared to the beginning of the semester, where an assortative mixing based on local features was dominant, the number of friendship through network-wide connections between students has increased by a factor of 20. Conclusion: Engaging the students in social learning environment improves the quantity and quality of student–student and student–instructor relationships, which facilitates the spread of knowledge and tackles social problems, such as isolation.

Assessment of using hydrogen in gas distribution grids

Substantial changes in the energy system are necessary to achieve greenhouse gas neutrality. Green hydrogen is a key to defossilisation. Politicians frequently mention the use of hydrogen in the building sector to supply decentrally produced heat as a potential field of application. An advantage repeatedly mentioned is that the existing gas distribution network infrastructure is an important asset that could still be used in the future. However, there is a lack of analyses of the conversion of gas distribution networks to hydrogen focussing on the economic implications on the costs of the distribution network infrastructure. The paper provides insights using a techno-economic model network analysis (MNA) tool called gas Distribution grId modelliNg tOol (DINO). The analysis is carried out for Germany and considers hydrogen use in all counties. The results are compared to a synthetic methane and electrification scenario. In the hydrogen scenario, the total need for distribution grids is decreasing until 2050 by at least 130,000 km. The network length of the synthetic methane scenario is slightly lower and that of the electrification scenario drops to zero. The annual operation costs are lower in all scenarios as gas demand and infrastructure are reduced. Nevertheless, the total annual cost in the hydrogen scenario is potentially two times higher than in the case of the synthetic methane scenario and more than four times higher than in the electrification scenario. Based on the present results, it is questionable whether an advantage of the continued use of the existing gas distribution grid infrastructure in case of synthetic gas or hydrogen scenarios exists.

Discourse about higher education on Twitter in early phases of COVID-19: A crisis management social network analysis.

The online version contains supplementary material available at 10.1007/s10639-023-11590-2.

A Bibliometric Study of Emerging Trends in The Equity Options Segment

Index options and stock options are part of equity options segment in particular and the equity derivatives segment in general. This study examines the evolution of index options and stock options as modern financial instruments along with open interest. 759 studies were reviewed from 1982 to 2022. The equity options market informational content is particularly helpful in forecasting future prices. As a result, it is a favourite of traders and short-term investors, but this subject needs more research. The study used a bibliometric and network analysis tool to identify the most cited studies, leading journals, and authors. The thematic structure of research on equity options and open interest has been inferred using the bibliometric analyses in this review paper. With the help of the Scopus database, the results have been summarized, covering the broad topics of volatility, options and option prices, stochastic models, financial markets and stock markets, hedging, Granger causality, price determination, model test, information asymmetry, equity and the VIX index. During this review study, researchers found that day traders and short-term investors can use the information in open interest, index options, and stock options to predict future prices.

A Model for the Network of Relief Centers during an Earthquake in the Central Zone of Tehran, Iran

INTRODUCTION: The network of relief centers, which is a connected set of relief centers, rescue points, and rescue routes, is of utmost importance during an earthquake. This study aimed to model this network to identify the aid-receiving points and rescue routes, followed by the development of plans to improve the aid-giving process during an earthquake. METHODS: This descriptive-analytical study was conducted based on an applied research method and a quantitative approach. The required data were obtained through the library method (documents). The data used in this study included location information of relief centers (hospitals, crisis management centers, Red Crescent centers) and the transportation routes among them. These pieces of information were collected from related plans and documents. The obtained data related to the location of relief centers were analyzed using Spatial Analysis, and Network Analysis tools were utilized to analyze the transportation routes among the relief centers. FINDINGS: The findings in this study revealed the right places for receiving aid and the rescue routes that deliver aid from the aid-giving to the aid-receiving centers in the central zone of Tehran in the shortest time. Moreover, the relief centers were ranked based on their importance during a crisis. CONCLUSION: The results of this study show the number of required aid-receiving points in the central zone of Tehran through which the aid needed during a crisis can be delivered from rescue centers. The findings revealed which relief centers played a more significant role during an earthquake. The identification of these points and routes makes the provision of special planning possible during an earthquake.

Integration of Single-Cell RNA-Sequencing and Network Analysis to Investigate Mechanisms of Drug Resistance.

Innate resistance and therapeutic-driven development of resistance to anticancer drugs is a common complication of cancer therapy. Understanding mechanisms of drug resistance can lead to development of alternative therapies. One strategy is to subject drug-sensitive and drug-resistant variants to single-cell RNA-seq (scRNA-seq) and to subject the scRNA-seq data to network analysis to identify pathways associated with drug resistance. This protocol describes a computational analysis pipeline to study drug resistance by subjecting scRNA-seq expression data to Passing Attributes between Networks for Data Assimilation (PANDA), an integrative network analysis tool that incorporates protein-protein interactions (PPI) and transcription factor (TF)-binding motifs.

Democratizing electricity distribution network analysis

Abstract The uptake of electric vehicles (EVs) and renewable energy technologies is changing the magnitude, variability, and direction of power flows in electricity networks. To ensure a successful transition to a net zero energy system, it will be necessary for a wide range of stakeholders to understand the impacts of these changing flows on networks. However, there is a gap between those with the data and capabilities to understand electricity networks, such as network operators, and those working on adjacent parts of the energy transition jigsaw, such as electricity suppliers and EV charging infrastructure operators. This paper describes the electric vehicle network analysis tool (EVENT), developed to help make network analysis accessible to a wider range of stakeholders in the energy ecosystem who might not have the bandwidth to curate and integrate disparate datasets and carry out electricity network simulations. EVENT analyses the potential impacts of low-carbon technologies on congestion in electricity networks, helping to inform the design of products and services. To demonstrate EVENT’s potential, we use an extensive smart meter dataset provided by an energy supplier to assess the impacts of electricity smart tariffs on networks. Results suggest both network operators and energy suppliers will have to work much more closely together to ensure that the flexibility of customers to support the energy system can be maximized, while respecting safety and security constraints within networks. EVENT’s modular and open-source approach enables integration of new methods and data, future-proofing the tool for long-term impact.

Designing the International Network of Tér és Forma, 1928–1939. A Hungarian Architectural Journal’s Data-Driven Analysis

Architectural periodicals were the major means of transferring textual and visual information about the current production, discourses, and problems of architecture during the interwar era. Illustrated magazines were widely available, and the immaterial sites of architectural publications became equally important as the construction site itself. In interwar Hungary, the architectural journal Tér és Forma (Space and Form) was the major organ of modern architecture under the editorship of the architect Virgil Bierbauer between 1928 and 1942. The periodical included the latest examples of modern architecture in Hungary and the current international scene covering most of Europe with an outlook on the USA, South America, and Japan. Bierbauer relied on his extensive international network of professional connections for transferring information and creating content for his journal. My paper focuses on the digital processing and analysis of Tér és Forma using the Croatian Artists Networks Information System (CAN_IS) as a digital network analysis tool. It allows the representation of the international relations of the journal based on its content, and the changes in editorial directions and its social network. My paper considers the methodology of organising the information extracted from the system and how this knowledge can be visualised. My paper also addresses the problems of legibility and distortion in data visualisations.

Ten-tier and multi-scale supply chain network analysis of medical equipment: random failure & intelligent attack analysis

Motivated by the COVID-19 pandemic, this paper explores the supply chain viability of medical equipment, an industry whose supply chain was put under a crucial test during the pandemic. This paper includes an empirical network-level analysis of supplier reachability under Random Failure Experiments (RFE) and Intelligent Attack Experiments (IAE). Specifically, this study investigates the effect of RFE and IAE across multiple tiers and scales. The global supply chain data was mined and analysed from about 45,000 firms with about 115,000 intertwined relationships spanning across 10 tiers of the backward supply chain of medical equipment. This complex supply chain network was analysed at four scales, namely: firm, country-industry, industry, and country. A notable contribution of this study is the application of a supply chain tier optimisation tool to identify the lowest tier of the supply chain that can provide adequate resolution for the study of the supply chain pattern. We also developed data-driven-tools to identify the thresholds for breakdown and fragmentation of the medical equipment supply chain when faced with random failures or different intelligent attack scenarios. The novel network analysis tools utilised in the study can be applied to the study of supply chain reachability and viability in other industries.

Activation of Transposable Elements in Human Skeletal Muscle Fibers upon Statin Treatment.

High cholesterol levels have been linked to a high risk of cardiovascular diseases, and preventative pharmacological care to lower cholesterol levels is critically important. Statins, which are hydroxymethylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors, are drugs used to reduce the endogenous cholesterol synthesis, thus minimizing its pathophysiological effects. Despite the proven benefits, statins therapy is known to cause a number of skeletal muscle disorders, including myalgia, myopathy and myositis. The mechanisms underlying such statin-induced side effects are unknown. Recently, a group of genes and molecular pathways has been described to participate in statin-induced myopathy, caused by either simvastatin or rosuvastatin, although the mechanism by which changes in gene regulation occur was not studied. Transposable Elements (TEs), repetitive elements that move within the genome, are known to play regulatory roles in gene expression; however, their role in statin-induced muscle damage has not been studied. We analyzed the expression of TEs in human skeletal fiber cells treated with either simvastatin or rosuvastatin, as well as their respective controls, and identified TEs that change their expression in response to the treatment. We found that simvastatin resulted in >1000 differentially expressed (DE) TEs, whereas rosuvastatin resulted in only 27 DE TEs. Using network analysis tools, we predicted the impact of the DE TEs on the expression of genes and found that amongst the genes potentially modulated by TEs, there are some previously associated to statin-linked myopathy pathways (e.g., AKT3). Overall, our results indicate that TEs may be a key player in the statin-induced muscle side effects.

Functional brain networks in the evaluation of patients with neurodegenerative disorders.

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as amarker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

타투합법화에 대한 타투이스트의 수용태도와 창업의도와의 네트워크 관계 분석

This study attempted to study the effects of the connection relationships between each variable by analyzing the network of relationships between the tattooist's acceptance attitude and startup intention. For this study, the data collected was interpreted through a social network analysis tool. The findings are as follows. First, for the relationship between the tattooist's acceptance attitude and the start-up intention, tattoo legalization was interpreted as significant factor influencing the start-up intention. Second, it was found that tattooists are willing to start a business, even if tattoos are not legalized. Third, despite giving happiness or pleasure to tattooists, tattoos are not yet prepared or considered due to social issues, such as tattoo legislation, confirming that initial tattoo-related startups are necessary to increase tattoo-related startups. Fourth, tattooists are expecting the legalization of tattoos within three years, and it was found that they planned their start-up intention according to that expectation. In addition, it was confirmed that tattoos are an act of giving pleasure to acquaintances around them, and these actions are recognized as correct. Finally, it was found that tattooists are in the stage of starting a business or collecting information for starting a business when tattooing is legalized.

Investigating geographical accessibility and site suitability of medical laboratories in Kermanshah-Iran.

One of the major challenges in developing countries is the inappropriate spatial distribution of medical laboratory centers (MLCs) which can lead to injustice in access to health services. This study aimed to investigate the accessibility to and site suitability of MLCs in Kermanshah Metropolis by GIS. In this cross-sectional study, data were collected from the Iran Statistical Center and Deputy of Treatment of Kermanshah University of Medical Sciences. We used Arc/GIS 10.6 software, AHP technique, and network analysis tools to determine the access status of Kermanshah citizens to MLCs in 2019 and site selection for MLCs. The layers used in this study included population density, city development trends, compatible and incompatible land uses, pathways, land slope, river area, and access radius. About 70% of households had inappropriate access to all MLCs in walking scenario. This ratio was 31.26% for 5 min, 9.58% for 10 min, and 6.09% for 15 min driving. Comparisons between public and private MLCs showed that in walking scenario, 88% of households had improper access to public and 80% to private MLCs. Based on 5 and 10 min of driving, 57 and 19% of households had inappropriate access to public MLCs, and 45 and 17% to private MLCs, respectively. Also, with 15 min of driving, 8% of households had improper access to public and 18% to private MLCs. Findings showed that scores provided for population density criteria were (0.298), distance from existing laboratories (0.213), proximity to pathways (0.175), consistent land use (0.129), city development trend (0.087), distance from riverfront (0.053), distance from incompatible land uses (0.015), and land slope (0.03). The final model was obtained by overlaying the layers. The model showed a 9-degree range from very bad to very good in Kermanshah city for the construction of laboratory centers (CR<0.01). The site selection model showed that the location of the proposed centers can be in the north and outskirts of the city to facilitate citizens' access to the MLCs. These results emphasize the justice in the spatial distribution of MLCs for the benefit of deprived populations as a global value.