Conventional Analysis Tools Research Articles

Transient stability monitoring and preventive control based on CCT distribution factor

AbstractThis paper proposes a monitoring and control method for transient stability (TS). First, it is clarified that there may exist various critical patterns of uncertain power flows causing instability due to photovoltaic generations. Then, the use of critical clearing time (CCT) is suggested as a TS index, which can be computed by conventional TS analysis tools. A distribution factor (DF) for controlling CCT is investigated, referred to as CCT‐DF in this paper. Then, three optimization problems using CCT‐DF are formulated based on bilevel optimization to obtain reliable operation planning. The combined use of the optimizations guarantees the power system robust security under uncertainties.

Hidden Extreme Multistability, Antimonotonicity and Offset Boosting Control in a Novel Fractional-Order Hyperchaotic System Without Equilibrium

Recently, the notion of hidden extreme multistability and hidden attractors is very attractive in chaos theory and nonlinear dynamics. In this paper, by utilizing a simple state feedback control technique, a novel 4D fractional-order hyperchaotic system is introduced. Of particular interest is that this new system has no equilibrium, which indicates that its attractors are all hidden and thus Shil’nikov method cannot be applied to prove the existence of chaos for lacking hetero-clinic or homo-clinic orbits. Compared with other fractional-order chaotic or hyperchaotic systems, this new system possesses three unique and remarkable features: (i) The amazing and interesting phenomenon of the coexistence of infinitely many hidden attractors with respect to same system parameters and different initial conditions is observed, meaning that hidden extreme multistability arises. (ii) By varying the initial conditions and selecting appropriate system parameters, the striking phenomenon of antimonotonicity is first discovered, especially in such a fractional-order hyperchaotic system without equilibrium. (iii) An attractive special feature of the convenience of offset boosting control of the system is also revealed. The complex and rich hidden dynamic behaviors of this system are investigated by using conventional nonlinear analysis tools, including equilibrium stability, phase portraits, bifurcation diagram, Lyapunov exponents, spectral entropy complexity, and so on. Furthermore, a hardware electronic circuit is designed and implemented. The hardware experimental results and the numerical simulations of the same system on the Matlab platform are well consistent with each other, which demonstrates the feasibility of this new fractional-order hyperchaotic system.

EXPLORING SIMILARITIES AND VARIATIONS OF HUMAN MOBILITY PATTERNS IN THE CITY OF LONDON

Abstract. The availability of new spatial data represents an unprecedented opportunity to better understand and plan cities. In particular, extensive data sets of human mobility data supply new information that can empower urbanism research to unveil how people use and visit urban places over time, overcoming traditional limitations related to the lack of large, detailed data sets. In this work, we explore patterns of similarities and spatial differences in human mobility flows in London, analysing their temporal variations in relation to the liveliness measured in a number of places. Using data sourced from the Oyster smart card and Twitter, we perform a time-series cluster analysis, exploring the similarity of temporal trends amongst places assigned to each cluster. Results suggest that differences in patterns appear to be related to the central and peripheral location of places, which present two or more temporal trends over the week. The type of transport network connecting the places (Tube, Railways, etc.) also appears to be a factor in determining significant differences. This work contributes to current urbanism research investigating the daily rhythms in cities. It also explores how to use mobility data to classify places according to their temporal features, with the aim of enhancing conventional analysis tools and integrating them with new quantitative information and methods.

Comparing practice-ready forecast models for weekly and monthly fluctuations of average daily traffic and enhancing accuracy by weighting methods

Abstract Knowing daily traffic for the current year is recognized as being essential in many fields of transport analysis and practice, and short-term forecasting models offer a set of tools to meet these needs. This paper examines and compares the accuracy of three representative parametric and non-parametric prediction models, selected by the analysis of the numerous methods proposed in the literature for their good combination of forecast accuracy and ease of calibration, using real-life data on Italian motorway stretches. Non-parametric K-NN regression model, Gaussian maximum likelihood model and double seasonality Holt–Winters exponential smoothing model confirm their goodness to predict the weekly and monthly fluctuations of average daily traffic with varying degrees of performance, while maintaining an easy use in professional practice, i.e. requiring ordinary professional skills and conventional analysis tools. Since combining several prediction models can give, on average, more accuracy than that of the individual models, the paper compares two weighting methods of easy implementation and susceptible to a direct use, namely the widely used information entropy method and the less widespread Shapley value method. Despite being less common than the information entropy method, the Shapley value method proves to be more capable in better combining single forecasts and produces improvements in the predictions for test data. With these remarks, the paper might be of interest to traffic technicians or analysts, in various and not uncommon tasks they might find in their work.

Three new gemini ionic liquids: Synthesis, characterizations and anticorrosion applications

Three gemini ionic liquids namely, 3,3′-(1,4-phenylenebis(methylene))bis(1-alkyl-1H-imidazol-3-ium)bromide IL1, IL2, and IL3, were prepared and characterized via the conventional tools of analysis, elemental analysis, IR, and 1H NMR spectroscopy. The thermal stability of these compounds was studied, using thermogravimetric analysis (TGA) technique. The gemini ionic liquids were tested as corrosion inhibitors for stainless steel (304 SS) in 0.5 M H2SO4 solution using electrochemical techniques. The Results showed that gemini ionic liquids were effective with optimal corrosion inhibition values of 90.7%, 97.3% and 82.6% for IL1, IL2 and IL3, respectively. The data confirmed also gemini ionic liquids are mixed type inhibitors.

A novel Heat Exchanger Network Bridge Retrofit method using the Modified Energy Transfer Diagram

The aim of this paper is to develop a novel method for the retrofit of Heat Exchanger Networks based on Bridge Retrofit analysis. The method identifies Retrofit Bridges that correspond to energy saving modifications using two new proposed tools: The Heat Surplus-Deficit Table and the Modified Energy Transfer Diagram. These tools both allow the identification and quantification of Retrofit Bridges. These tools have been developed following conventional Pinch Analysis tools such as the Composite Curve and Grand Composite Curve. The connection between the conventional Heat Exchanger Network synthesis tools and the proposed retrofit tools is established to improve understanding of the method and relate it to what is currently used in both literature and industry. The method is demonstrated with a simple illustrative example and a more detail paper mill case study. The paper mill incorporates a paper machine and paper recycling plant and is co-located with a Kraft pulp mill in New Zealand. Results from the retrofit method suggest a retrofit design that will achieve an annualised profit of NZD 570,000/y (USD 414,000/y) with a payback of 2.4 y.

Hybrid Reliability Analysis Framework for Reliability Analysis of Tunnels

Numerical modelling of tunnels, generally carried out by using some numerical analysis tools [e.g., finite element (FE) method, discrete element method], is often computationally expensive. Hence, it becomes extremely difficult to perform reliability analysis of such models using conventional reliability analysis tools. This necessitates development of efficient techniques for reliability analysis. In this work, a novel framework, referred to as a hybrid reliability analysis framework (HRAF), is developed for reliability analysis of tunnels. The proposed approach utilizes a hybrid polynomial correlated function expansion (H-PCFE), a distribution adaptive sequential experimental design (DA-SED), and an adaptive algorithm for further refining the estimates of DA-SED based H-PCFE. The primary idea of HRAF is to use DA-SED based H-PCFE in zones where the probability of misclassification is less and to use actual simulation for zones with a higher probability of misclassification. As a consequence, results obtained are highly accurate and, at that too, from a reasonably lower number of training points and/or actual simulations. Two tunnel problems have been presented to illustrate the performance of the proposed approach. Results obtained have been benchmarked against results obtained using a full-scale Monte Carlo simulation (MCS) with 105 sample points. Results obtained indicate the excellent performance (both in accuracy and efficiency) of the proposed framework.

A novel high-sensitivity assay to detect a small fraction of mutant IDH1 using droplet digital PCR.

Detection of mutations in the isocitrate dehydrogenase 1 (IDH1) gene is useful for accurate diagnosis of lower grade gliomas, as described in the 2016 World Health Organization classification of tumors of the central nervous system. Conventional analysis tools, including Sanger DNA sequencing and immunohistochemistry, might fail to detect a small fraction of mutant IDH1 owing to their limited sensitivity. Considering that lower grade gliomas are infiltrative in nature, a highly sensitive detection assay for IDH1 mutation is required for their accurate diagnosis. In this study, we successfully established a droplet digital PCR (ddPCR) system to detect a small fraction of IDH1 mutation. We could detect 0.05% of mutant IDH1 allele in 30ng DNA. Using this assay, we could detect a small fraction of mutant IDH1 in a glioma case, identified as a wildtype tumor according to the conventional assays. Additionally, in a small amount of DNA derived from the cerebrospinal fluid, we could detect an IDH1 mutation. In conclusion, the ddPCR system is useful to identify a small fraction of IDH1 mutation in diffuse infiltrative gliomas. This might be useful for precision medicine of these gliomas in the near future and also for the non-invasive diagnosis of these gliomas.

Synthesis and study antimicrobial activities of some novel Tetrazole derivatives

Four (2-phenol derivatives) were coupled with 1H-tetrazole-5-diazonium chloride to synthesize four new compounds namely Ta-d. These prepared Azophenols reacted with two 4-alkoxy acids (8 and 10) to form two new series of 4-((1H-tetrazol-5-yl) diazenyl)-2-(R)-phenyl-4-(octyloxy) benzoates T8a-d and 4-((1H-tetrazol-5-yl)diazenyl)-2-(R)-phenyl-4-(decyloxy) benzoates T10a-d respectively. The structures of the twelve synthesized compounds were confirmed using the conventional tools of analysis, Elemental Analysis, FT-IR and 1H-NMR spectroscopy. They were tested as anti-fungi and anti-bacteria. Most of the prepared compounds possessed good antimicrobial action for Gram Positive and Gram-Negative Bacteria, whereas they gave no action on Aspergillus Fumigatus or Candida albicans.

Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks

The next-generation wireless networks are evolving into very complex systems because of the very diversified service requirements, heterogeneity in applications, devices, and networks. The mobile network operators (MNOs) need to make the best use of the available resources, for example, power, spectrum, as well as infrastructures. Traditional networking approaches, i.e., reactive, centrally-managed, one-size-fits-all approaches and conventional data analysis tools that have limited capability (space and time) are not competent anymore and cannot satisfy and serve that future complex networks in terms of operation and optimization in a cost-effective way. A novel paradigm of proactive, self-aware, self- adaptive and predictive networking is much needed. The MNOs have access to large amounts of data, especially from the network and the subscribers. Systematic exploitation of the big data greatly helps in making the network smart, intelligent and facilitates cost-effective operation and optimization. In view of this, we consider a data-driven next-generation wireless network model, where the MNOs employ advanced data analytics for their networks. We discuss the data sources and strong drivers for the adoption of the data analytics and the role of machine learning, artificial intelligence in making the network intelligent in terms of being self-aware, self-adaptive, proactive and prescriptive. A set of network design and optimization schemes are presented with respect to data analytics. The paper is concluded with a discussion of challenges and benefits of adopting big data analytics and artificial intelligence in the next-generation communication system.

Exploratory study and application of the angular wavelet analysis for assessing the spatial distribution of breakdown spots in Pt/HfO2/Pt structures

The angular wavelet analysis is applied for assessing the spatial distribution of breakdown spots in Pt/HfO2/Pt capacitors with areas ranging from 104 to 105 μm2. The breakdown spot lateral sizes are in the range from 1 to 3 μm, and they appear distributed on the top metal electrode as a point pattern. The spots are generated by ramped and constant voltage stresses and are the consequence of microexplosions caused by the formation of shorts spanning the dielectric film. This kind of pattern was analyzed in the past using the conventional spatial analysis tools such as intensity plots, distance histograms, pair correlation function, and nearest neighbours. Here, we show that the wavelet analysis offers an alternative and complementary method for testing whether or not the failure site distribution departs from a complete spatial randomness process in the angular domain. The effect of using different wavelet functions, such as the Haar, Sine, French top hat, Mexican hat, and Morlet, as well as the roles played by the process intensity, the location of the voltage probe, and the aspect ratio of the device, are all discussed.

Unraveled Face-Dependent Effects of Multilayered Graphene Embedded in Transparent Organic Light-Emitting Diodes.

With increasing demand for transparent conducting electrodes, graphene has attracted considerable attention, owing to its high electrical conductivity, high transmittance, low reflectance, flexibility, and tunable work function. Two faces of single-layer graphene are indistinguishable in its nature, and this idea has not been doubted even in multilayered graphene (MLG) because it is difficult to separately characterize the front (first-born) and the rear face (last-born) of MLG by using conventional analysis tools, such as Raman and ultraviolet spectroscopy, scanning probe microscopy, and sheet resistance. In this paper, we report the striking difference of the emission pattern and performance of transparent organic light-emitting diodes (OLEDs) depending on the adopted face of MLG and show the resolved chemical and physical states of both faces by using depth-selected absorption spectroscopy. Our results strongly support that the interface property between two different materials rules over the bulk property in the driving performance of OLEDs.

GPU-Accelerated Rendering Methods to Visually Analyze Large-Scale Disaster Simulation Data

Emerging methodologies for natural hazard risk assessments involve the execution of a multitude of different interacting simulation models that produce vast amounts of spatio-temporal datasets. This data pool is further enlarged when such simulation results are post-processed using GIS operations, for example to derive information for decision-making. The novel approach presented in this paper makes use of the GPU-accelerated rendering pipeline to perform such operations on-the-fly without storing any results on secondary memory and thus saving large amounts of storage space. Particularly, algorithms for three frequently used geospatial analysis methods are provided, namely for the computation of difference maps using map algebra and overlay operations, distance maps and buffers as examples for proximity analyses as well as kernel density estimation and inverse distance weighting as examples for statistical surfaces. In addition, a visualization tool is presented that integrates these methods using a node-based data flow architecture. The application of this visualization tool to the results of a real-world risk assessment methodology used in civil engineering shows that the memory footprint of post-processing datasets can be reduced at the order of terabytes. Although the technique has several limitations, most notably the reduced interoperability with conventional analysis tools, it can be beneficial for other use cases. When integrated into desktop GIS applications, for example, it can be used to quickly generate a preview of the results of complex analysis chains or it can reduce the amount of data to be transferred to web or mobile GIS applications.

Static Modeling of Microgrids for Load Flow and Fault Analysis

Inverter interfaced distributed generators (DGs) in microgrids have different characteristics and models that are not available in the existing conventional power flow analysis tools. This paper presents a static modeling approach for inverter interfaced DGs that can be applied for time spread load flow analysis and fault analysis of microgrids, including droop-based voltage controlled DGs. The static models have been derived from the common control schemes applied to inverter interfaced DGs, including the constraints emerging from droop control and reflect steady-state behaviors of inverters accurately. In addition to simplification of analysis procedure, the static models can provide a base for the analysis of microgrids with conventional numeric analysis tools. The presented static modeling approach has been validated comparatively with the dynamic modeling results of a test microgrid.

Investigating the efficiency of newly prepared imidazolium ionic liquids for carbon dioxide removal from natural gas

Four functionalized ionic liquids of the type 3-(n-aminoalkyl)-1,2-dimethyl imidazolium bis((trifluoromethyl)sulfonyl)imide (IL101 and IL102), and 3-(n-aminoalkyl)-1,2-dimethyl imidazolium tetrafluoroborate(IL103 and IL104) were synthesized and characterized via the conventional tools of analysis. The application of these compounds as potential industrial solvents for carbon dioxide removal from natural gas is studied. In our work we used bubbling technique, as chemical absorption one, for CO2 removal by chemical means involves one or more reversible chemical reactions between CO2 and another material to produce a liquid or solid species. The efficiency of synthesized ionic liquids for capturing carbon dioxide is ranked as follows, IL104<IL103<IL102<IL101.

Synthesis and Applications of Pyridazinones for Base oil Improvement

Three Pyridazinone derivatives of the type 4,5-dihydropyridazin-3(2H)-ones, N-(4-(6-oxo-5-((5-phenyl-1,3,4-thiadiazol-2-yl)amino)-1,4,5,6-tetrahydropyridazin-3-yl)phenyl) acetamide (4a), 6-(4-Chlorophenyl)-4-((5-phenyl-1,3,4-thiadiazol-2-yl)amino)-4,5-di-hydropyridazin-3(2H)-one (4b) and 6-(4-Bromophenyl)-4-((5-phenyl-1,3,4-thiadiazol-2-yl)amino)-4,5-dihydropyridazin-3(2H)-one (4c) were synthesized. They were characterized by the conventional tools of analysis, Elemental analysis, IR and 1H-NMR spectroscopy. The tools of analysis confirmed the structure of the three prepared compounds. These heterocyclic compounds are chemically stable and possess multi actions for base oil improvement. They are tested as antioxidants for local base oil through the change in total acid number (TAN). They gave good results as antioxidants base oil. Also these three synthesized compounds are tested as corrosion inhibitors for carbon steel in acid medium. The efficiency order for these tested compounds is ranked as follows: 4a > 4b > 4c. Energy of the highest occupied molecular orbital (EHOMO) and lowest unoccupied molecular orbital (ELUMO) for the three prepared compounds were calculated via the Ab initio method. Studying of the quantum chemical calculations of the synthesized compounds showed good matching with the experimental results.

Linear State-Space Representation of the Axial Dynamics of Electrodynamic Thrust Bearings

Electrodynamic bearings can guide rotating objects without contact and active control means. Consequently, their performance relies on passive phenomena that require specific models to be predicted. In the case of electrodynamic thrust bearings, early models involved assumptions on the rotor axial kinematics. This precludes their use for predicting the bearing behavior in dynamic conditions, which is critical when studying stability aspects. Other models require numerically solving of Faraday’s current law and the rotor equation of motion to obtain the dynamics of the bearing, thereby excluding a fast identification of its stability properties. Recently, a parametric model without these kinematic assumptions was derived. It consists in a linear state-space representation that allows studying the dynamic performance of electrodynamic thrust bearings using conventional system analysis tools. However, this model links up the rotor displacement to the currents in a winding with only two phases. This paper introduces a model free of this limitation, allowing to consider bearings with a higher number of phases, an improved copper usage, and potentially increased performance. Furthermore, the proposed model directly links up the rotor displacement to the force and torque exerted by the thrust bearing without requiring to solve for the currents. This also minimizes the number of state-space equations and model parameters. Finally, in addition to the modeling aspects, a set of bearing topologies that have not been explored yet and can be studied using the proposed model is introduced.

148. AptaMetrics: A Web-Based Aptamer Bioinformatics Platform

RNA and DNA aptamer development as a therapeutic moiety and as a diagnostic tool is a rapidly growing field. The discovery process for aptamers involves iterative rounds of selection in a process termed SELEX, Systematic Evolution of Ligands by EXponentional enrichment. The modern SELEX process was revolutionized with the adoption of high-throughput sequencing (HTS), which is able to identify millions of aptamer sequences across multiple rounds of aptamer selection. The scale of aptamer HTS datasets has necessitated bioinformatics algorithms, such as clustering algorithms, to identify candidate aptamers for experimental validation. Herein we describe a web-based aptamer bioinformatics platform, AptaMetrics, which utilizes a collection of programs and algorithms to facilitate the analysis of aptamer HTS datasets.The AptaMetrics platform builds upon our previously described aptamer bioinformatics work (Thiel et. al. PLoSOne, 2012). Central to AptaMetrics is our clustering algorithm, ProcessSeqs, which we engineered to include several novel and distinct features: 1) clustering may be performed using either an “all-versus-all” or a “seed” sequence strategy; 2) clustering may be accomplished through either sequence similarity (edit distance) or structural similarity (tree distance); 3) the output of ProcessSeqs provides a visual and interactive representation of related aptamer networks; and 4) these networks of related aptamers may be analyzed using conventional network analysis tools. We also developed an aptamer structure prediction program, ProcessFold, that provides the predicted structure of millions of aptamer sequences utilizing a variety of proven oligonucleotide structure prediction algorithms (e.g., RNAfold, Mfold, RNAstructure). Integration of these two programs thereby allows users to create networks of structurally related aptamers based on the different structure prediction algorithms. We also created and incorporated an aptamer truncation algorithm, AptmR, into the AptaMetrics platform. The purpose of AptmR is to determine a predicted truncated aptamer sequence based on aptamer sequence and structure information provided by ProcessFold. AptmR can be applied to either a single aptamer sequence or to millions of aptamer sequences. The predicted truncations may then be clustered into networks of related aptamers using the ProcessSeqs clustering algorithm. Importantly, to allow the greatest versatility, each program and algorithm within AptaMetrics may be used independently or as a pipeline. The collection of programs and algorithms within AptaMetrics provide a significant degree of flexibility in analyzing aptamer HTS data while maintaining the ease of accessibility of a web-based environment.

Analysis of coupling dynamics for power systems with iterative discrete decision making architectures

Iterative “learning” by distributed control agents has been proposed for power system decision making. Such decision making can achieve agreement among control agents while preserving privacy. The iterative decision making process may interact with power system dynamics. In such cases, coupled dynamics are expected. The objective of this paper is to propose a modeling approach that can conduct stability analysis for these hybrid systems. In the proposed approach, the discrete decision making process is approximated by continuous dynamics. As a result, the entire hybrid system can be represented by a continuous dynamic system. Conventional stability analysis tools are then used to check system stability and identify key impacting factors. An example power system with multiple control agents is used to demonstrate the proposed modeling and analysis. The analysis results are then validated by nonlinear time-domain simulation.The continuous dynamics models developed in this paper sheds insights into the control nature of each distributed optimization algorithm. An important finding is documented in this paper: a consensus algorithm based decision making may act as an integrator of frequency deviation. It can bring the frequency back to nominal while the primal-dual based decision making cannot.

Learning from Uncertainty for Big Data: Future Analytical Challenges and Strategies

Big data refers to data sets that are so large that conventional database management and data analysis tools are insufficient to work with them [1]. Presently, we are in an era of big data, which exists in various fields, such as social media, telecom, finance, medicine, biinformatics, and power networks. Big data results mainly from the evolutionary development of data storage and data collection techniques in recent years. Big data has become a bigger-than-ever problem with the quick developments of data collection technologies. In fact, the word big is a fuzzy concept. So far, we do not have a mathematical definition of big data. But we can use several features or coordinates to describe it, for example, the well-known 5V characteristics.