Cyclic Complexes Research Articles

Coinage-Metal-Based Cyclic Trinuclear Complexes with Metal-Metal Interactions: Theories to Experiments and Structures to Functions.

Among the d10 coinage metal complexes, cyclic trinuclear complexes (CTCs) or trinuclear metallocycles with intratrimer metal-metal interactions are fascinating and important metal-organic or organometallic π-acids/bases. Each CTC of characteristic planar or near-planar trimetal nine-membered rings consists of Au(I)/Ag(I)/Cu(I) cations that linearly coordinate with N and/or C atoms in ditopic anionic bridging ligands. Since the first discovery of Au(I) CTC in the 1970s, research of CTCs has involved several fundamental areas, including noncovalent and metallophilic interaction, excimer/exciplex, acid-base chemistry, metalloaromaticity, supramolecular assemblies, and host/guest chemistry. These allow CTCs to be embraced in a wide range of innovative potential applications that include chemical sensing, semiconducting, gas and liquid adsorption/separation, catalysis, full-color display, and solid-state lighting. This review aims to provide a historic and comprehensive summary on CTCs and their extension to higher nuclearity complexes and coordination polymers from the perspectives of synthesis, structure, theoretical insight, and potential applications.

Identification of most critical paths using sparse matrix in software testing

A software code may practically consist of many functions or subroutines with loops and branches. Also, the quantification of cyclomatic complexity is tedious, when the size of the software code is too large. Practically, size of the code for a real time application is generally large and dd-graph generation and maneuverability are tedious. Exhaustive testing is impractical and therefore it is not feasible to test all possible paths in the flow graph. Moreover, it is difficult to identify the uncovered portion, identification of most critical paths and hence unable to test all critical components in the software code. The present investigation focuses to display decisions along test paths in dd-graph of the software code using sparse matrix approach drawn through MATLAB biograph object module. In this approach, the concept of dd-graph (decision-to-decision graph) is taken from control flow graph of the software code by joining decision to decision. An identification of most critical paths and test case generation are done using artificial bee colony optimization. To avoid the testing complexity, the sparse matrix approach is used to demonstrate the most critical paths and display of the dd-graph using biograph object module by initializing the edge-node relationships is presented. The gcov code coverage analysis generates branch percentage probability as coverage summary which is considered as edge weights of the sparse matrix. This present approach is tested for the benchmark problem of “finding roots of the quadratic equation” software code.

Dissecting molecular network structures using a network subgraph approach.

Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.

Decomposition of Ruthenium Olefin Metathesis Catalyst

Ruthenium olefin metathesis catalysts are one of the most commonly used class of catalysts. There are multiple reviews on their uses in various branches of chemistry and other sciences but a detailed review of their decomposition is missing, despite a large number of recent and important advances in this field. In particular, in the last five years several new mechanism of decomposition, both olefin-driven as well as induced by external agents, have been suggested and used to explain differences in the decomposition rates and the metathesis activities of both standard, N-heterocyclic carbene-based systems and the recently developed cyclic alkyl amino carbene-containing complexes. Here we present a review which explores the last 30 years of the decomposition studied on ruthenium olefin metathesis catalyst driven by both intrinsic features of such catalysts as well as external chemicals.

Early Detection of Cascade Flutter in a Model Aircraft Turbine Using a Methodology Combining Complex Networks and Synchronization

We report an experimental study on the early detection of cascade flutter in a model aircraft turbine using a methodology that combines complex networks and synchronization. The network topology constructed from the determinism in the cross-recurrence plot allows us to extract the primary hubs during a transition to cascade flutter. The node strength in the network topology and the synchronization parameter are valid for capturing a precursor of cascade flutter.

Realization of Reliability for Data and Software to Improve Quality

The goal is to look for code performance metrics. Reliability is an important aspect of any program that cannot be ignored and difficult to measure. “Program reliability is defined as the probability of running programs without disruption in a specific environment for a specified period of time.” The reliability of the technology differs from the performance of the hardware. Program reliability is difficult because the complexity of the program is high. Different methods can be used to increase system performance, but it is difficult to balance development time, budget, and software quality. But the best way to ensure technology consistency is to build high-quality programs throughout the life cycle of the program. We will discuss software reliability metrics in this paper. Metrics used early on can help detect and correct defects of requirements that will prevent program lifecycle errors later. It also provides consistency quality of the information system database with the help of RStudio, and we can also illustrate reliability based on the value of cyclomatic complexity and we can say whether the data or software is more reliable, less reliable or somewhat reliable.

Prediction and classification of sleep quality based on phase synchronization related whole-brain dynamic connectivity using resting state fMRI

Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t-test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification.

Kuramoto model in the presence of additional interactions that break rotational symmetry.

The Kuramoto model serves as a paradigm to study the phenomenon of spontaneous collective synchronization. We study here a nontrivial generalization of the Kuramoto model by including an interaction that breaks explicitly the rotational symmetry of the model. In an inertial frame (e.g., the laboratory frame), the Kuramoto model does not allow for a stationary state, that is, a state with time-independent value of the so-called Kuramoto (complex) synchronization order parameter z≡re^{iψ}. Note that a time-independent z implies r and ψ are both time independent, with the latter fact corresponding to a state in which ψ rotates at zero frequency (no rotation). In this backdrop, we ask: Does the introduction of the symmetry-breaking term suffice to allow for the existence of a stationary state in the laboratory frame? Compared to the original model, we reveal a rather rich phase diagram of the resulting model, with the existence of both stationary and standing wave phases. While in the former the synchronization order parameter r has a long-time value that is time independent, one has in the latter an oscillatory behavior of the order parameter as a function of time that nevertheless yields a nonzero and time-independent time average. Our results are based on numerical integration of the dynamical equations as well as an exact analysis of the dynamics by invoking the so-called Ott-Antonsen ansatz that allows to derive a reduced set of time-evolution equations for the order parameter.

White-box software test generation with Microsoft Pex on open source C# projects: A dataset.

The paper presents a dataset on software tests generated using the Microsoft Pex (IntelliTest) test generator tool for 10 open source projects. The projects were selected randomly from popular GitHub repositories written in C#. The selected projects contain 7187 methods from which Pex was able to generate tests for 2596 methods totaling 38,618 lines of code. Data collection was performed on a cloud virtual machine. The dataset presents metrics about the attributes of the selected projects (e.g., cyclomatic complexity or number of external method calls) and the test generation (e.g., statement and branch coverage, number of warnings). This data is compared to an automated isolation technique in the paper Automated Isolation for White-box Test Generation [1]. To the best of our knowledge, this is the largest public dataset about the test generation performance of Microsoft Pex on open source projects. The dataset highlights current practical challenges and can be used as a baseline for new test generation techniques.

The Harmonic Decomposition in Cyclic Homology

It has been shown by Cuntz and Quillen that in characteristic 0 the kernel of the square of the “noncommutative Laplacian” on the Hochschild and cyclic complexes contains the relevant homology information. In this note, we show that the same property holds for the plain kernel of this Laplacian, as in differential geometry. Using the same ideas, we define a variant of Hochschild homology and cyclic homology and show that we recover the classical definitions in characteristic 0.

Complex bifurcation analysis and synchronization optimal control for Hindmarsh-Rose neuron model under magnetic flow effect.

In this contribution, the complex behaviour of the Hindmarsh-Rose neuron model under magnetic flow effect (mHR) is investigated in terms of bifurcation diagrams, Lyapunov exponent plots and time series when varying only the electromagnetic induction strength. Some exciting phenomena are found including, for instance, various firings patterns by applying appropriate magnetic strength and Hopf-fold bursting through fast-slow bifurcation. In addition to this, the interesting phenomenon of Hopf bifurcation is examined in the model. Thus, we prove that Hopf bifurcation occurs in this memristor-based HR neuron model when an appropriately chosen magnetic flux varies and reaches its critical value. Furthermore, one of the main results of this work was the optimal control approach to realize the synchronization of two mHR. The main advantage of the proposed optimal master-slave synchronization from a control point of view is that, in the practical application, the electrical activities (quiescent, bursting, spiking, period and chaos states) of a neuron can be regulated by a pacemaker (master) associated with biological neuron (slave) to treat some diseases such as epilepsy. A suitable electronic circuit is designed and used for the investigations. PSpice based simulation results confirm that the electrical activities and synchronization between coupled neurons can be modulated by electromagnetic flux.

Study of p-(3-carboxy-1-adamantyl)-calix[4]arene with hydrogen bonds along the upper and lower rim by IR spectroscopy and DFT.

The IR spectra of p-(3-carboxy-1-adamantyl)-calix[4]arene (AdC4A) were studied. Using IR spectroscopy, it has been shown that in calixarene in dilute solution in CCl4, there was no free hydroxyl absorption band. The hydroxyl group band was characterized by a very low frequency, which indicates a strong intramolecular hydrogen bond on the lower rim of the calixarene molecules. On the upper rim of the calixarene, the carboxyl groups form cyclic dimer or tetramer complexes via intermolecular hydrogen bonds. The cone conformation persists, but there is a mutual influence of the hydrogen bonds along the top and bottom rims of the calixarene molecule. The structure with dimeric hydrogen bonds between the carboxyl groups is 16.5KJ/mol more preferable than the structure with tetrameric cyclic hydrogen bonds for AdC4A. The reactivity depends on the type of association on the upper rim, whether these are hydrogen-bonded dimers or cyclic tetrameric association. Graphical abstract .

An efficiency-based multi-state system for reliable power delivery combining renewable sources

This paper presents a solar-wind-battery uninterruptible power supply system, which improves reliability issues at the same time that reduces the electric energy bill. The system allows transparent commutation/combination among generators using a DC diode-based OR-gate scheme taking advantage of the renewable-sources nature and avoiding complex synchronization algorithms for the AC regime. The use of a diode-based structure is justified because an efficiency analysis shows that the associated efficiency of the scheme is close to 96%. Given the geographical location, the use of the solar resource is prioritized over other generators, whose operating voltage is therefore adjusted using DC/DC converters. Since a solar panel maximum power voltage is in fact a dynamic parameter, a fuzzy-logic controller is implemented to adjust the DC/DC converters output, which in turn minimizes grid consumption. Testing results show that the controller works properly with high accuracy and precision. Since reliability and grid consumption reduction are targeted, an automatic configuration agent is incorporated to operate the system in one of four predefined states. In tests spanning a complete day with blackout scenarios, the system can provide about 40% more energy to the users with a total grid consumption reduction close to 30%.

The structure of benzonitrile-water complex as unveiled by matrix isolation infrared spectroscopy: Is it linear or cyclic at low temperatures?

The hydrogen bonded interaction of benzonitrile (C6H5CN)-water (H2O) complexes have been studied experimentally using matrix isolation infrared spectroscopy to elucidate the structure(s) of the binary complexes produced at low temperatures. Computations performed at MP2 and B3LYP-D3 level of theory with aug-cc-pVDZ basis set predicted two complexes, of which global minimum complex A has a cyclic structure with the interactions occurring between ortho-hydrogen of C6H5CN and oxygen of H2O and the hydrogen of H2O with π-cloud of CN in C6H5CN. The linear acylic complex B, a local minimum is stabilized through a straight C-H⋯N interaction between nitrogen of C6H5CN and hydrogen of H2O and is present at a relative energy of ∼0.7 kcal/mol with respect to global minimum. While previous gas phase studies using a number of spectroscopic probes identify exclusively the cyclic global complex, our experiments performed under isolated conditions at low temperatures uniquely produced the acyclic linear C6H5CN-H2O complex, a local minimum in the potential energy surface. The CN stretching vibrational mode of C6H5CN was exploited as a unique marker to confirm the generation of local minimum. The effect of matrixes was simulated using Onsager self-consistent reaction field model and as a result, the linear complex (complex B) turned out to be energetically favored isomer under the influence of matrixes. The hydrogen bonding interaction in both the complexes was characterized using Atoms in Molecules, Natural Bond Orbital, Energy Decomposition and Non-Covalent Interaction analyses.

Ionic Dissociation of SiCl4 : Formation of [SiL6 ]Cl4 with L=Dimethylphosphinic Acid.

Reactions of SiCl4 with R2PO(OH) (R=Me, Cl) yield compounds with six‐fold coordinated silicon atoms. Whereas R=Me afforded the hexacoordinated tetra‐cationic silicon complex [Si(Me2PO(OH))6]4+ with chloride counter‐ions, R=Cl caused release of HCl with formation of a cyclic dimeric silicon complex [Si(Cl2PO(OH))(Cl2PO2)3(μ‐Cl2PO2)]2 with bridging bidentate dichlorophosphates.

Business Process Model Variations in Islamic Boarding Schools Using Requirements Analysis Method

The business process at a boarding school is a collection of all the detailed activities that have been arranged in such a way as to solve the existing problems so that the objectives set can be met. There are development standards that can be used as a reference to build architecture and business process models at a boarding school. Every business process has planned many ways to be able to obtain goals precisely. Therefore variations are formed in the business process models. Business process models of variations are a collection of business process models that have the same goals in different ways. Variations in the business process models are needed to achieve the right goals in a variety of ways according to existing resources and needs. From all reviews of the variations, it is necessary to have a cyclomatic complexity to find out how complex the business process models is and to maintain the simplicity of the business process models. This study uses the requirements analysis method, which is the intensive interaction stage between the system analyst and the system user community (end-user), where the system development team shows its expertise to get user feedback and confidence so that it gets good participation. The results of this study are a collection of variations of business process models that have a level of complexity not much different from one variation, with another on each standard applied.

Design Complexity for Objective Function Points

This paper investigates correlating the basic elements of Unified Modeling Language and Cyclomatic Complexity with Function Point Analysis (FPA) principles to develop an automated software functional sizing tool. This concept has been difficult to achieve due to the logical nature of the FPA sizing methodology versus the physical nature of source lines of code (SLOC). In this approach, we examine software complexity from design and maintainability perspectives in order to understand relationships in physical code. Our hypothesis is that this method will “simulate” FPA principles and produce an objective sizing method. This would provide the foundation for an automated tool that scans physical software code to derive “Objective Function Points”(OFPs) functional size measure.

A Non-Adaptive Single-Phase PLL Based on Discrete Half-Band Filtering to Suppress Severe Frequency Disturbances

The interconnection of new generating and storing devices to the power grid imposes the necessity of synchronizing, so the power flow can be manipulated and distributed. In the presence of an increasingly perturbed electric grid, many proposals of novel and modified synchronization techniques attained enough robustness to deal with known perturbations. However, such proposals exhibit drawbacks on their own, leaving open enhancement opportunities, mostly over their discrete implementation—e.g., sampling issues and not-considered inter/harmonics—and their inherent complexity—e.g., the need for frequency adaptability. In this work, three traditional synchronous reference frame (SRF) phase-locked loops (PLL) are modified to implement discrete filtering, such as the well-known proposals based on moving average filters (MAFs), to avoid the problems mentioned above, known for affecting the MAF’s performance. This proposal makes use of discrete, efficient units modularly assembled to yield a signal’s average, based on elliptic half-band filters. The proposed PLLs were tested and exhibited clear advantages—robustness against frequency disturbances—over MAF-based equivalents at standardized tests over a typical simulation environment, setting through this work an initial milestone for its verification and further incorporation in more complex synchronization topologies.

Comparison of Machine Learning Techniques for Software Quality Prediction

Software quality prediction is one the most challenging tasks in the development and maintenance of software. Machine learning (ML) is widely being incorporated for the prediction of the quality of a final product in the early development stages of the software development life cycle (SDLC). An ML prediction model uses software metrics and faulty data from previous projects to detect high-risk modules for future projects, so that the testing efforts can be targeted to those specific ‘risky' modules. Hence, ML-based predictors contribute to the detection of development anomalies early and inexpensively and ensure the timely delivery of a successful, failure-free and supreme quality software product within budget. This article has a comparison of 30 software quality prediction models (5 technique * 6 dataset) built on five ML techniques: artificial neural network (ANN); support vector machine (SVMs); Decision Tree (DTs); k-Nearest Neighbor (KNN); and Naïve Bayes Classifiers (NBC), using six datasets: CM1, KC1, KC2, PC1, JM1, and a combined one. These models exploit the predictive power of static code metrics, McCabe complexity metrics, for quality prediction. All thirty predictors are compared using a receiver operator curve (ROC), area under the curve (AUC), and accuracy as performance evaluation criteria. The results show that the ANN technique for software quality prediction is promising for accurate quality prediction irrespective of the dataset used.

Impact of Design Patterns on Software Complexity and Size

Many different factors influence the quality of software. Among the most important of these factors is software complexity. One way to improve software quality, therefore, is to minimize its complexity making it more understandable and maintainable. The design phase of the software development life cycle plays an instrumental role in fostering quality in software. Seasoned designers often use past design best practices codified in the form of design patterns to make their designs and the resultant code more elegant, robust, and resilient to change. Little work, however, has been done to empirically assess the quantitative impact of design patterns on software complexity. This research is an attempt to fill this gap. A comparative analysis of before and after versions of program pairs written without and with design patterns was performed for all twenty three GoF (Gang of Four) design patterns. These program pairs were collected (or, in some cases, developed) and compared with respect to their complexity and size. The results of this comparative analysis reveal that the cyclomatic complexity of the programs written using design patterns was less for most of the design patterns as compared to the programs written without using design patterns. However, the values of CK metrics, number of classes, and software size SLOC (Source Lines of Code) increased when design patterns were used.