Cyclic Complexes Research Articles

Security algorithms for distributed storage system for E-health application over wireless body area network

Wireless body area networks (WBANs) are an upcoming technology for achieving effective healthcare. The security and privacy of patient-related data are two essential aspects of WBAN system security. Storing data on a single server is simple but may lead to a single point of failure (whether a typical failure or a failure due to security attacks). In this paper, enhanced security and privacy of patient information and E-health systems based on distributed storage systems (DSSs) are developed using public-key cryptography to store patient information. The storage of data and the security requirements (such as confidentiality, reliability, authentication, and dynamic integrity) are simultaneously distributed among individual nodes throughout the network. The patient data are encrypted using the redundant residue number system (RRNS) technique, which depends on a library of moduli in the encrypting process to generate residues. Three algorithms were implemented to decode the RRNS: the Chinese Remainder Theorem (CRT), Base Extension (BEX) with mixed radix conversion, and the new Chinese Remainder Theorem I (CRT I). The results of a system prototype implementation showed that whenever the value of the moduli was a big prime number, the coding efficiency became large, and the time to send a message decreased by 2 s. The performance of the algorithms was expressed in terms of execution time and cyclomatic complexity (CC). The results showed that the CRT I was 1 s faster than the other decoding algorithms and took around 2.5 s to decode the RRNS. The CRT had a low CC of 15, whereas the MRC and CRT I had a CC of 45 and 40, respectively. The main operation phases of the proposed system are described.

Cyclic Heterometallic Interactions formed from a Flexible Tripeptide Complex Showing Effective Antiferromagnetic Spin Coupling

AbstractDeveloping tunable motifs for heterometallic interactions should be beneficial for fabricating functional materials based on cooperative electronic communications between metal centers. Reported here is the efficient formation of cyclic heterometallic interactions from a complex containing an artificial tripeptide with metal binding sites on its main chain and side chains. X‐ray structural analysis and X‐ray absorption spectroscopy revealed that the cyclic metal–metal arrangements arise from the amide groups connecting four square‐planar CuII centers and four octahedral NiII centers in a cyclic manner. UV/Vis spectral studies suggested that this efficient formation was achieved by the selective formation of the square‐planar CuII centers and a crystallization process. Magnetic measurements using SQUID clarified that the cyclic complex represented the S=2 spin state at low temperatures due to effective antiferromagnetic interactions between the NiII and CuII centers.

Stochastic synchronization of dynamics on the human connectome

Synchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network’s topological and dynamical properties. However, how these factors drive the emergence of synchronization in the presence of potentially disruptive external inputs like stochastic perturbations is not well understood, particularly for real-world systems such as the human brain. Here, we aim to systematically address this problem using a large-scale model of the human brain network (i.e., the human connectome). The results show that the model can produce complex synchronization patterns transitioning between incoherent and coherent states. When nodes in the network are coupled at some critical strength, a counterintuitive phenomenon emerges where the addition of noise increases the synchronization of global and local dynamics, with structural hub nodes benefiting the most. This stochastic synchronization effect is found to be driven by the intrinsic hierarchy of neural timescales of the brain and the heterogeneous complex topology of the connectome. Moreover, the effect coincides with clustering of node phases and node frequencies and strengthening of the functional connectivity of some of the connectome’s subnetworks. Overall, the work provides broad theoretical insights into the emergence and mechanisms of stochastic synchronization, highlighting its putative contribution in achieving network integration underpinning brain function.

Peptide Hydrolysis by Metal (Oxa)cyclen Complexes: Revisiting the Mechanism and Assessing Ligand Effects.

The mechanism responsible for peptide bond hydrolysis by Co(III) and Cu(II) complexes with (oxa)cyclen ligands has been revisited by means of computational tools. We propose that the mechanism starts by substrate coordination and an outer-sphere attack on the amide C atom of a solvent water molecule assisted by the metal hydroxo moiety as a general base, which occurs through six-membered ring transition states. This new mechanism represents a more likely scenario than the previously proposed mechanisms that involved an inner-sphere nucleophilic attack through more strained four-membered rings transition states. The corresponding computed overall free-energy barrier of 25.2 kcal mol-1 for hydrolysis of the peptide bond in Phe-Ala by a cobalt(III) oxacyclen catalyst (1) is consistent with the experimental values obtained from rate constants. Also, we assessed the influence of the nature of the ligand throughout a systematic replacement of N by O atoms in the (oxa)cyclen ligand. Increasing the number of coordinating O atoms accelerates the reaction by increasing the Lewis acidity of the metal ion. On the other hand, the higher reactivity observed for the copper(II) oxacyclen catalyst with respect to the analogous Co(III) complex can be attributed to the larger Brönsted basicity of the copper(II) hydroxo ligand. Ultimately, the detailed understanding of the ligand and metal nature effects allowed us to identify the double role of the metal hydroxo complexes as Lewis acids and Brönsted bases and to rationalize the observed reactivity trends.

Prediction of Mild Cognitive Impairment Using Movement Complexity.

Aimless movement or wandering may be a symptom of mild cognitive impairment (MCI) that arises as a consequence of confusion and forgetfulness. This paper presents a support vector machine (SVM) framework based on movement analysis for the prediction of the onset and progression of MCI. Movement data of 22 subjects with MCI, and 22 other healthy subjects, living independently in smart homes were collected for ten years using motion sensors. Features were extracted from the sensor data using movement metrics, including cyclomatic complexity, detrended fluctuation analysis, fractal index, entropy, and room transitions. Two different SVM classification algorithms were trained using the features, first to predict the progression of MCI in the post-transition period, and second to predict the onset of MCI in the pre-transition phase. The two SVMs were able to detect the onset six months earlier than the clinical diagnosis. The model accuracy in classifying MCI increased monotonically from the onset month and reached maximum (81%) at the 11th post-transition month. The features of cyclomatic complexity contributed significantly to the prediction results. Findings support the use of movement complexity measures and machine learning for monitoring cognitive behavior in an independent living environment.

Metric Based Evaluation and Improvement of Software Designs

The demand for quality software expands as the business environment grows internationally. Common to all software development methodologies is a design phase that focuses on the physical software model. Design criteria used to evaluate these models are important for refinement and improvement. The purpose of this research is to apply transformations of McCabe’s cyclomatic complexity as a means for improving software design and assessing risks within a design. In this research, two metrics, the management (CMGT) and maintenance (CMN) coefficients are introduced, and they are used to address the architectural, size, and quality dimensions of a software design.

Development of metallosupramolecular phosphatases based on the combinatorial self-assembly of metal complexes and organic building blocks for the catalytic hydrolysis of phosphate monoesters

The phosphorylation and dephosphorylation of proteins and other intracellular molecules are important reactions in living systems and are generally controlled by the action of kinases and phosphatases. The design and synthesis of artificial mimics of natural enzymes are important research subjects in bioorganic and bioinorganic chemistry. A supramolecular strategy based on the three-dimensional self-assembly of molecular building blocks would be useful for the construction of artificial supramolecular complexes. The main focus of this review is on artificial mimics of natural phosphatases that contain dimetallic cores in their active sites. Previous examples of artificial mimics of alkaline phosphatases (AP) constructed covalently are initially presented, then followed by a description of artificial metallosupramolecular complexes formed by the combinatorial self-assembly of different bis(Zn2+-cyclen) complexes containing 2,2′-bipyridyl (bpy) linker (cyclen = 1,4,7,10-tetraazacyclododecane), cyanuric acid or barbital (Bar) units, and Cu2+ ion in single or two-phase solvent systems. It is now thought that the appropriate combination of the various building blocks and solvent systems (single- and two-phase solvent systems) can strongly assist in developing the artificial systems that accelerate and catalyze the hydrolysis of a phosphate monoester, mono(p-nitrophenyl) phosphate (MNP). Such approaches would be useful in terms of understanding the mechanism of action of naturally occurring enzymes and to develop biologically active, bioorthogonal, and versatile supramolecular catalysts.

Surface-induced dissociation of protein complexes on a cyclic ion mobility spectrometer.

We describe the implementation of a simple three-electrode surface-induced dissociation (SID) cell on a cyclic ion mobility spectrometer (cIMS) and demonstrate the utility of multipass mobility separations for resolving multiple conformations of protein complexes generated during collision-induced and surface-induced unfolding (CIU & SIU) experiments. In addition to CIU and SIU, SID of protein complexes is readily accomplished within the native instrument software and with no additional external power supplies by entering a single SID collision energy, a simplification in user experience compared to prior implementations. A set of cyclic homomeric protein complexes and a heterohexamer with known CID and SID behavior were analyzed to investigate mass and mobility resolution improvements, the latter of which improved by 20–50% (median: 33%) compared to a linear travelling wave device. Multiple passes of intact complexes, or their SID fragments, increased the mobility resolution by an average of 15% per pass, with the racetrack effect being observed after ∼3 or 4 passes, depending on the drift time spread of the analytes. Even with modest improvements to apparent mobility resolving power, multipass experiments were particularly useful for separating conformations produced from CIU and SIU experiments. We illustrate several examples where either (1) multipass experiments revealed multiple overlapping conformations previously unobserved or obscured due to limited mobility resolution, or (2) CIU or SIU conformations that appeared ‘native’ in a single pass experiment were actually slightly compacted or expanded, with the change only being measurable through multipass experiments. The work conducted here, the first utilization of multipass cyclic ion mobility for CIU, SIU, and SID of protein assemblies and a demonstration of a wholly integrated SIU/SID workflow, paves the way for widespread adoption of SID technology for native mass spectrometry and also improves our understanding of gas-phase protein complex CIU and SIU conformationomes.

Isolation and studies of a thioether-functionalized pyrazole derived Cu(i)-based cyclic trinuclear complex and its coordination polymers with [Cu2I2] and [BiBr3] nodes

Emissive Cu(i)-CTC with unprecedented thioether SBUs have their photoluminescence shifted and quenched after assembly with CuI and BiBr3, respectively.

Improving the Trustworthiness of Software Through Rigorous Data Type Design

We discuss the importance of ensuring that software developers take a measured approach to making design decisions related to data. Ada can be used to reduce cyclomatic complexity to improve the trustworthiness of software.

SuccERP: The design science based integration of ECS and ERP in post-implementation stage

The existing studies of Enterprise Resource Planning (ERP) are primarily focusing on the adoption and implementation stages, the post-implementation stage has received less attention in comparison. However, most enterprises have been struggling under the post-implementation stage. This work aims to review the state-of-the-art issues of ERP in the post-implementation stage, including communication, legacy system, collaboration, and the manager is hard to monitor the performance. Based on the Design Science (DS) method, we highlight how to offset the lack of an ERP system and ECS according to the guidelines of DS, and show the exhaustive steps for implementing the artifact-SuccERP. Our research is rigorous and interpretive by considering the steps of the DS and the functions of Software Engineering. Further, we explore multiple ERP systems to summarize the difference in authentication, initial data, and specific procedures aspects, after that, we consider the two most popular procedures (order creation and bill of purchase creation) as examples to demonstrate and evaluate the proposed artifact—SuccERP in the result. We propose the complete and practical research for solving the issues from previous theoretical results of an ERP, and to show experimentally that the proposed SuccERP is easy to maintain by applying the Cyclomatic Complexity and the Maintainability Index as metrics. This study is a milestone that allows ERP research to move from the theoretical stage to integrating, creating things that serve a human purpose, and dealing with the issues presented by previous works practically.

A Cyclomatic Complexity Generalization for a Composite Service

A Cyclomatic Complexity Generalization for a Composite Service

Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine.

In view of the important position of crude oil in the national economy and its contribution to various economic sectors, crude oil price and volatility prediction have become an increasingly hot issue that is concerned by practitioners and researchers. In this paper, a new hybrid forecasting model based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed to forecast the daily prices and 7-day volatility of Brent and WTI crude oil. The KELM has the advantage of less time consuming and lower parameter-sensitivity, thus showing fine prediction ability. The effectiveness of VMD-KELM model is verified by a comparative study with other hybrid models and their single models. Except various commonly used evaluation criteria, a recently-developed multi-scale composite complexity synchronization (MCCS) statistic is also utilized to evaluate the synchrony degree between the predictive and the actual values. The empirical results verify that 1) KELM model holds better performance than ELM and BP in crude oil and volatility forecasting; 2) VMD-based model outperforms the EEMD-based model; 3) The developed VMD-KELM model exhibits great superiority compared with other popular models not only for crude oil price, but also for volatility prediction.

Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network.

Pre-Bötzinger complex (PBC) is a necessary condition for the generation of respiratory rhythm. Due to the existence of synaptic gaps, delay plays a key role in the synchronous operation of coupled neurons. In this study, the relationship between synchronization and correlation degree is established for the first time by using ISI bifurcation and correlation coefficient, and the relationship between synchronization and correlation degree is discussed under the conditions of no delay, symmetric delay, and asymmetric delay. The results show that the phase synchronization of two coupling PBCs is closely related to the weak correlation, that is, the weak phase synchronization may occur under the condition of incomplete synchronization. Moreover, the time delay and coupling strength are controlled in the modified PBC network model, which not only reveals the law of PBC firing transition but also reveals the complex synchronization behavior in the coupled chaotic neurons. Especially, when the two coupled neurons are nonidentical, the complete synchronization will disappear. These results fully reveal the dynamic behavior of the PBC neural system, which is helpful to explore the signal transmission and coding of PBC neurons and provide theoretical value for further understanding respiratory rhythm.

Reliable Low-Rank Approximation of Matrices Detection Aided Multicarrier DCSK Receiver Design

Multicarrier differential chaos shift keying (MC-DCSK) systems transmit the reference chaotic signal for information recovery to remove the complex synchronization circuit. However, transmission errors in the reference chaotic signal would induce the error propagations when directly used for demodulations. In order to address this issue, we propose a low-rank approximation of matrices (LRAM) detection method to improve the reliability performances. In our design, we exploit the low-rank characteristic of MC-DCSK signals sharing the reference chaotic signal. Instead of directly using the received reference chaotic signal for correlated demodulation, we propose to apply the LRAM method to jointly estimate the reference signal and the information-bearing signal. We use the singular value decomposition and generalized LRAM methods to minimize the distances between the estimates and transmitted data. Thus, the signal-to-noise ratio (SNR) can be improved to achieve lower bit error rate (BER). Moreover, theoretical BER expression is derived and LRAM-aided system is proved to be capable of proving the maximum-likelihood detection for received signals. Simulation results demonstrate the BER performances of our design outperform counterparts over additive white Gaussian noise and fading channels.

Two-dimensional lanthanide(III) cyclic coordination polymers complexes containing dimethyl phosphate ligand: Synthesis, spectroscopic characterization, thermal analysis, and crystal structures

Three new two-dimensional lanthanide(III) cyclic coordination polymers (CPs), [Ln(μ-DMP)3]n (Ln is Ce (1), Nd (2) and Sm (3); and DMP is dimethyl phosphate), were prepared from the reaction of LnCl3.XH2O (X is 7 in 1 and is 0 in 2 and 3) salts and trimethyl phosphate (TMP) ligand in mixture of H2O/CH3OH (1:10) at 40°C. Suitable crystals of these complexes for crystal structure determination were obtained by slow evaporation of the produced solutions at room temperature. The three complexes were characterized by elemental analysis, IR, 1H-NMR, 3C{1H}-NMR and 31P-NMR spectroscopy and their structures were studied by the single-crystal diffraction method. The X-ray structural analysis indicated that all three complexes have two-dimensional cyclic polymer structure. Furthermore, in complexes of 1 and 3, the Ce(III) and Sm(III) cations are six-coordinated in a distorted octahedral configuration by six bridged bidentate dimethyl phosphate ligands, while in complex 2, the Nd(III) cation is seven-coordinated in a distorted mono-capped octahedral configuration by four bridged bidentate dimethyl phosphate and two bridged tridentate dimethyl phosphate ligands. Furthermore, the thermal stability of all three complexes was studied by thermogravimetric and differential thermal analyses.

Automation of information distribution in adaptive electronic document management systems using machine learning

Introduction. Electronic document management systems (EDMS) are used to store, process and transmit large amounts of information. Automation of these processes is a challenge that requires a comprehensive solution. Its solution willreduce the time and material costs for design and make the transition to a more advanced, adaptive EDMS. The paper is devoted to the development of new methods for automating the process of distributing information in the EDMS. The work objective is to improve the accuracy of the information distribution in the EDMS through moving from analytical or algorithmic solutions to the use of new methods based on machine learning technologies. The application of neural networks in the furtherance of this purpose will also improve the efficiency of software development through automating the analysis and processing of information. Materials and Methods. A new method of the automated information distribution based on machine learning technologies including a mathematical description of the information distribution rules is proposed. The formulated list of conditions for the information distribution provides the implementation of software based on neural networks for solving the problem of automatic data distribution in the EDMS. Results. The method of automated information distribution has been tested on the example of the EDMS subject area when solving the problem of analyzing the correctness of information entered by the user. In the course of experimental studies, it was found that the proposed method, based on machine learning technologies, provides better accuracy (8 % higher) and is more efficient (in accordance with the Jilb metrics and cyclomatic complexity). Discussion and Conclusions. The results obtained confirm the efficiency and accuracy of the method proposed. The presented results can be used to automate the processes of distribution and verification of information in adaptive EDMS, as well as in other information systems. Based on the method developed, it is also possible to solve connected problems: search for duplicates and similar documents, classification and placement by file categories.

Multifaceted infrastructure for self-adaptive IoT systems

Abstract Background: Internet of Things (IoT) enables the interaction among objects to provide services to their users. Areas such as eHealth, smart energy, and smart buildings have been benefiting from the IoT potential. However, the development of IoT systems is still complex because it deals with a highly dynamic, volatile, and heterogeneous environment. These characteristics require discovering devices, managing these devices’ context, and self-adapt their behavior. Goal : In this work, we propose a self-adaptive IoT infrastructure to support multiple facets, i.e., the contextual discovery of smart objects, the context management, and the self-adaptation process of the development of these systems. Methods : We evaluated the proposed infrastructure by developing a smart building application with and without it. The evaluation focused on four issues: the feasibility of integrating the context management through middleware platforms with adaptation based on workflows in a request/response communication model, the impact of our infrastructure on the development of self-adaptive IoT systems considering cyclomatic complexity and coupling code metrics; the impact of using contextual filters on the orchestrator of self-adaptation; and the impact on the quality of the self-adaptation. Results : The results suggest that: (i) it is feasible to use the proposed infrastructure in the development of self-adaptive IoT systems; (ii) there is a reduction in the cyclomatic complexity and the coupling with our approach, (iii) there is a considerable decrease in the number of rules evaluated at runtime, (iv) our infrastructure reduces the execution time of the adaptations when using contextual filters, and (v) the self-adaptation process was effective when using the orchestrator of self-adaptations. Conclusion : With these results, we observed that the proposed multifaceted infrastructure could reduce the complexity related to the development of IoT systems, in addition to optimizing their self-adaptation process.

Occasional stabilisation of limit cycle walking and control of chaos in the passive dynamics of the compass-gait biped model

It is known that the compass-gait biped model exhibits cyclic walking patterns and attractive complex phenomena, namely chaos and bifurcations. This work is concerned with the stabilisation of limit cycles and then the control of chaos. A new stabilisation process of the limit cycle walking is developed based on self-detection of the fixed point of the (un)stable limit cycle and on energy-shaping-based trajectory-tracking controller. The control process is applied in the beginning of the swing stage during a desired short time interval making hence the compass-gait biped robot to be completely passive on the remaining swing phase. We demonstrate that such occasional stabilisation of the limit cycles considerably increases the energetic efficiency of the bipedal locomotion. We show also that the proposed control method allows the compass-gait biped to walk efficiently and with a periodic gait down sloped surfaces of different angles.

Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting

The load forecasting of a coal mining enterprise is a complicated problem due to the irregular technological process of mining. It is necessary to apply models that can distinguish both cyclic components and complex rules in the energy consumption data that reflect the highly volatile technological process. For such tasks, Artificial Neural Networks demonstrate advanced performance. In recent years, the effectiveness of Artificial Neural Networks has been significantly improved thanks to new state-of-the-art architectures, training methods and approaches to reduce overfitting. In this paper, the Recurrent Neural Network architecture with a small-size model was applied to the short-term load forecasting of a coal mining enterprise. A single recurrent model was developed and trained for the entire four-year operational period of the enterprise, with significant changes in the energy consumption pattern during the period. This task was challenging since it required high-level generalization performance from the model. It was shown that the accuracy and generalization properties of small-size recurrent models can be significantly improved by the proper selection of the hyper-parameters and training method. The effectiveness of the proposed approach was validated using a real-case dataset.