Structure of Graphene, and Mechanical and Bonding Characteristics of Single Wall Carbon Nanotube by Linear Scaling Quantum Mechanical Method

Using a linear scaling self-consistent-charge density functional tight binding （SCC-DFTB） and an ab initio Omol method, the bonding characteristics and Young＇s modulus of （10, 0） and （10,10） single-walled carbon nanotubes are calculated. The structure of a graphene is also calculated. It is found that the C-C and C-H bond length, their distribution characteristics on the tube, and Young^s modulus of the tube by linear scaling SCC-DFTB are identical to those by ab initio, while the computing cost by the linear scaling SCC-DFTB is reduced by more than 30 times as compared with that by the Dmol for the （10,0） and （10,10） tubes. By computing the structure of a graphene it is also found that the linear scaling SCCDFTB is reliable and time-saving.

Linear scaling Coulomb interaction in the multiwavelet basis,a parallel implementation

We present a parallel and linear scaling implementation of the calculation of the electrostatic potential arising from an arbitrary charge distribution.Our approach is making use of the multi-resolution basis of multiwavelets.The potential is obtained as the direct solution of the Poisson equation in its Green’s function integral form.In the multiwavelet basis,the formally non local integral operator decays rapidly to negligible values away from the main diagonal,yielding an effectively banded structure where the bandwidth is only dictated by the requested accuracy.This sparse operator structure has been exploited to achieve linear scaling and parallel algorithms.Parallelization has been achieved both through the shared memory(OpenMP)and the message passing interface(MPI)paradigm.Our implementation has been tested by computing the electrostatic potential of the electronic density of long-chain alkanes and diamond fragments showing(sub)linear scaling with the system size and efficent parallelization.

Linear Scaling Discontinuous Galerkin Density Matrix Minimization Method with Local Orbital Enriched Finite Element Basis:1-D Lattice Model System

In the first of a series of papers,wewill study a discontinuous Galerkin(DG)framework for many electron quantum systems.The salient feature of this framework is the flexibility of using hybrid physics-based local orbitals and accuracy-guaranteed piecewise polynomial basis in representing the Hamiltonian of the many body system.Such a flexibility is made possible by using the discontinuous Galerkin method to approximate the Hamiltonian matrix elements with proper constructions of numerical DG fluxes at the finite element interfaces.In this paper,we will apply the DG method to the density matrix minimization formulation,a popular approach in the density functional theory of many body Schrodinger equations.The density matrix minimization is to find the minima of the total energy,expressed as a functional of the density matrixρ(r,r′),approximated by the proposed enriched basis,together with two constraints of idempotency and electric neutrality.The idempotency will be handled with theMcWeeny’s purification while the neutrality is enforced by imposing the number of electrons with a penalty method.A conjugate gradient method(a Polak-Ribiere variant)is used to solve the minimization problem.Finally,the linear-scaling algorithm and the advantage of using the local orbital enriched finite element basis in the DG approximations are verified by studying examples of one dimensional lattice model systems.

Atomic Dispersed Hetero‑Pairs for Enhanced Electrocatalytic CO_(2)Reduction

Electrochemical carbon dioxide reduction reaction(CO_(2)RR)involves a variety of intermediates with highly correlated reaction and ad-desorption energies,hindering optimization of the catalytic activity.For example,increasing the binding of the*COOH to the active site will generally increase the*CO desorption energy.Breaking this relationship may be expected to dramatically improve the intrinsic activity of CO_(2)RR,but remains an unsolved challenge.Herein,we addressed this conundrum by constructing a unique atomic dispersed hetero-pair consisting of Mo-Fe di-atoms anchored on N-doped carbon carrier.This system shows an unprecedented CO_(2)RR intrinsic activity with TOF of 3336 h−1,high selectivity toward CO production,Faradaic efficiency of 95.96%at−0.60 V and excellent stability.Theoretical calculations show that the Mo-Fe diatomic sites increased the*COOH intermediate adsorption energy by bridging adsorption of*COOH intermediates.At the same time,d-d orbital coupling in the Mo-Fe di-atom results in electron delocalization and facilitates desorption of*CO intermediates.Thus,the undesirable correlation between these steps is broken.This work provides a promising approach,specifically the use of di-atoms,for breaking unfavorable relationships based on understanding of the catalytic mechanisms at the atomic scale.

Asymptotic analysis on weakly forced vibration of axially moving viscoelastic beam constituted by standard linear solid model

The weakly forced vibration of an axially moving viscoelastic beam is inves- tigated. The viscoelastic material of the beam is constituted by the standard linear solid model with the material time derivative involved. The nonlinear equations governing the transverse vibration are derived from the dynamical, constitutive, and geometrical relations. The method of multiple scales is used to determine the steady-state response. The modulation equation is derived from the solvability condition of eliminating secular terms. Closed-form expressions of the amplitude and existence condition of nontrivial steady-state response are derived from the modulation equation. The stability of non- trivial steady-state response is examined via the Routh-Hurwitz criterion.

Novel Block Chain Technique for Data Privacy and Access Anonymity in Smart Healthcare

The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internetand computing resources. In recent years, many more IoT applications have beenextensively used. For instance, Healthcare applications execute computations utilizing the user’s private data stored on cloud servers. However, the main obstaclesfaced by the extensive acceptance and usage of these emerging technologies aresecurity and privacy. Moreover, many healthcare data management system applications have emerged, offering solutions for distinct circumstances. But still, theexisting system has issues with specific security issues, privacy-preserving rate,information loss, etc. Hence, the overall system performance is reduced significantly. A unique blockchain-based technique is proposed to improve anonymityin terms of data access and data privacy to overcome the above-mentioned issues.Initially, the registration phase is done for the device and the user. After that, theGeo-Location and IP Address values collected during registration are convertedinto Hash values using Adler 32 hashing algorithm, and the private and publickeys are generated using the key generation centre. Then the authentication is performed through login. The user then submits a request to the blockchain server,which redirects the request to the associated IoT device in order to obtain thesensed IoT data. The detected data is anonymized in the device and stored inthe cloud server using the Linear Scaling based Rider Optimization algorithmwith integrated KL Anonymity (LSR-KLA) approach. After that, the Time-stamp-based Public and Private Key Schnorr Signature (TSPP-SS) mechanismis used to permit the authorized user to access the data, and the blockchain servertracks the entire transaction. The experimental findings showed that the proposedLSR-KLA and TSPP-SS technique provides better performance in terms of higherprivacy-preserving rate, lower information loss, execution time, and Central Processing Unit (CPU) usage than the existing techniques. Thus, the proposed method allows for better data privacy in the smart healthcare network.

Experimental studies of linear phased-array transmitter acoustic logging in cased wells with scaled borehole models

Laboratory cased-hole acoustic logging simulations are developed with the linear phased-array transmitter in scaled cased well models for evaluating the feasibility of extracting formation acoustic parameters through casing.The full waveforms are measured with different cement bonding models.By analyzing the measured wavetrains and the time-slowness correlation graphs,it is showed that when the generation conditions of the refracted compressional wave and the refracted shear wave are reached successively by regulating the direction of acoustic beam radiated from the linear phased-array transmitter,steered angle of the main radiation lobe with both good bonding interfaces.The refracted compressional wave and the refracted shear wave can be stimulated obviously and the casing wave can be suppressed effectively,even when the casing and cement（or the cement and formation） is not bonded.Based on these observations, it is worthwhile to apply the linear phased-array transmitter to determine formation velocities,particularly in poorly bonded cased well.The works establish the experimental and theoretical foundation for new generation cased-hole acoustic logging tool development.

Mellin Transform-Based Correction Method for Linear Scale Inconsistency of Intrusion Events Identification in OFPS

For the problem that the linear scale of intrusion signals in the optical fiber pre-warning system （OFPS） is inconsistent, this paper presents a method to correct the scale. Firstly, the intrusion signals are intercepted, and an aggregate of the segments with equal length is obtained. Then, the Mellin transform （MT） is applied to convert them into the same scale. The spectral characteristics are obtained by the Fourier transform. Finally, we adopt back-propagation （BP） neural network to identify intrusion types, which takes the spectral characteristics as input. We carried out the field experiments and collected the optical fiber intrusion signals which contain the picking signal, shoveling signal, and running signal. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the intrusion signals.

Theoretical study of the low-lying electronic excited states for molecular aggregates

We present here a brief summary of a National Natural Science Foundation Major Project entitled "Theoretical study of the low-lying electronic excited state for molecular aggregates". The project focuses on theoretical investigation of the electronic structures and dynamic processes upon photo-and electric-excitation for molecules and aggregates. We aim to develop reliable methodology to predict the optoelectronic properties of molecular materials related to the electronic excitations and to apply in the experiments. We identify two essential scientific challenges: (i) nature of intramolecular and intermolecular electronic excited states; (ii) theoretical description of the dynamic processes of the coupled motion of electronic excitations and nucleus. We propose the following four subjects of research: (i) linear scaling time-dependent density-functional theory and its application to open shell system; (ii) computational method development of electronic excited state for molecular aggregates; (iii) theoretical investigation of the time evolution of the excited state dynamics; (iv) methods to predict the optoelectronic properties starting from electronic excited state investigation for organic materials and experimental verifications.

Parameter adjustment based on improved genetic algorithm for cognitive radio networks

Multi-objective parameter adjustment plays an important role in improving the performance of the cognitive radio （CR） system. Current research focus on the genetic algorithm （GA） to achieve parameter optimization in CR, while general GA always fall into premature convergence. Thereafter, this paper proposed a linear scale transformation to the fitness of individual chromosome, which can reduce the impact of extraordinary individuals exiting in the early evolution iterations, and ensure competition between individuals in the latter evolution iterations. This paper also introduces an adaptive crossover and mutation probability algorithm into parameter adjustment, which can ensure the diversity and convergence of the population. Two applications are applied in the parameter adjustment of CR, one application prefers the bit error rate and another prefers the bandwidth. Simulation results show that the improved parameter adjustment algorithm can converge to the global optimal solution fast without falling into premature convergence.