Low Computational Cost Method Research Articles

Challenges of Protein-Protein Docking of the Membrane Proteins.

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have beensignificant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features andthe augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

Assessing Low-Cost Computational Methods against Structural Properties and Size Effects of Pt nanoparticles

Assessing Low-Cost Computational Methods against Structural Properties and Size Effects of Pt nanoparticles

Torque-Based Control of a Bio-Inspired Modular Climbing Robot

This article presents a generalizable, low computational cost, simple, and fast gravity compensation method for legged robots with a variable number of legs. It is based on the static problem, which is a reduction in the dynamic model of the robot that takes advantage of the low velocity of climbing robots. To solve it, we propose a method that computes the torque to be applied by each actuator to compensate for the gravitational forces without using the Jacobian matrix for the forces exerted by the end-effector and without using analytical methods for the gravitational components of the model. We compare our method with the most popular method and conclude that ours is twice as fast. Using the proposed gravity compensator, we present a torque-based PD controller for the position of the leg modules, and a body velocity control without dynamic compensation. In addition, we validate the method with both hardware and a simulated version of the ROMERIN robot, a modular legged and climbing robot. Furthermore, we compare our controller with the usual kinematic inverse controllers, demonstrating that the mean angular and linear error is significantly reduced, as well as the power requirements of the actuators.

Exploring the spatial correlation in radio tomographic imaging by block‐structured sparse Bayesian learning

Radio Tomographic Imaging (RTI) is a low-cost computational imaging method realised by the Radio Frequency (RF) signal sensing. The target-induced shadowing effect in the RF sensing network is reconstructed as a probability image to estimate the target's position. Then, the RTI-based Device-free Localization (DFL) is becoming a promising research topic in the Location-based Services applications by the Internet of Things (IoT). However, the multipath interference in the RF sensing network often induces the imaging degradation and decreases the DFL accuracy. To deal with the multipath-induced imaging degradation, considering that the target's shadowing occupies a small spatial range in the RF network and expresses some spatial structure, this article explores the spatial correlation in the target's shadowing. Then, a new RTI reconstruction method based on the Structured Sparse Bayesian Learning is proposed to model the spatial correlation implied in the sparse target's shadowing image. Further, the localisation experiments in actual scenes are conducted to validate the utilisation of the spatial correlation in target's shadowing is able to improve the imaging quality of the RTI system by enhancing the robustness towards the multipath-induced imaging degradation.

Data-Driven Multiobjective Optimization of Wave-Packets for Near-Field Subsonic Jet Noise

Aeroacoustics of innovative aircraft cannot disregard the development of low-order models for the jet noise source, which are essential to assess the propulsion–airframe interactions from the conceptual design stage. The main scope of this work is to provide a noise source model that can be coupled with relatively low computational cost methods for aeroacoustic scattering. To this end, this paper presents for the first time a multiobjective optimization of the zeroth-mode wave-packet in the jet near field. The importance of calibrating the model with near-field pressure data stems from the fact that in new aircraft, the engine nacelles are typically positioned at a few diameters from the wing or fuselage. In this work, the near field of a high subsonic jet at a Mach number of 0.9 is represented as a cylindrical surface radiating the pressure disturbances of a wave-packet source, which is optimized using large-eddy simulation data from three lines at different radial distances. The optimized model has been tested at Strouhal numbers between 0.25 and one, and the optimized solutions have been chosen using a Pareto-ranking criterion considering the wave-packet prediction over an extra line. A good agreement is achieved between the reference data and model predictions for multiple near-field radial distances.

The Effectiveness of Narrowing the Window size for LD & HD EMG Channels based on Novel Deep Learning Wavelet Scattering Transform Feature Extraction Approach.

The use of the Electromyogram (EMG) signals as a source of control to command externally powered prostheses is often challenged by the signal complexity and non-stationary behavior. Mainly, two factors affect classification accuracy: selecting the optimum feature extraction methods and overlapping segmentation/window size. Nowadays, studies attempt to use deep learning (DL) methods to improve classification accuracy. However, DL models are frequently hampered by their requirements of a vast quantity of training data to attain decent performance and the high computing costs. Therefore, researchers tried to replace the deep learning models with other low computational cost methods like deep wavelet scattering transform (DWST) as a feature extraction technique. In terms of windows size, selecting a larger window size increases the classification accuracy, but at the same time, it increases the processing time, which makes the system unsuitable for real-time applications. Accordingly, researchers attempted to minimise the size of the overlapping windows as much as possible without impacting classification performance. This work suggests to utilise DWST transform to achieve two goals (a) extracting the features from EMG signal with low computational cost. Even though many studies have used DWST approaches to extract features from other biological signals, but not been examined before for EMG signals. (b) study the effect of extracting the features from high-density EMG datasets (HD EMG) and low-density EMG datasets (LD EMG), reducing the analysis window size by up to 32msec with minimal impact on classification performance. The outcomes of the proposed method are compared with other well-known feature extraction algorithms to validate these achievements. The proposed strategy exceeds other methods by more than 25% in accuracy.

A low-cost computational method for solving nonlinear fractional delay differential equations

The present work is devoted to proposing a low-cost spectral method based on the modified hat functions for solving fractional delay differential equations. The fractional derivative is considered in the sense of Caputo. In order to solve the considered problem, the existing functions in it are approximated using the basis functions. By employing some important properties of the basis functions, Caputo derivative and Riemann–Liouville fractional integral, the main problem is transformed into some systems of nonlinear algebraic equations including two unknown parameters. This procedure mainly simplifies the problem and gives its approximate solution after solving the resulting systems. In addition, the computational complexity of the derived system is investigated. An error analysis is discussed to show the convergence order of the method. Finally, the suggested technique is applied to some sample problems with the aim of checking its validity and accuracy.

The conformational landscape of α-aminoglycine

The conformational landscape of α-aminoglycine has been studied. A conformational search has been first done using a set of different low-cost computational methods. The conformers obtained have been finally optimized at MP2 and B3LYP-D3/6–311++G(2d,p) levels of theory. A conformational landscape of 15 conformers with relative energies lower than 2000 cm−1 has been obtained. For the global minimum (IIa) a cooperative network formed by an OH···NH, NH···N and NH···OC hydrogen bonds, closing a sequential cycle stabilizes the structure. The next conformer in energy (Ia) also has two NH···OC, and NH··N and NH···OH hydrogen bonds. The potential energy surfaces associated with different torsions have been investigated, to rationalize the stability of some conformers. The relative population ratio in a supersonic jet has been estimated to calculate the relative intensity of the rotational spectra of the predicted conformers.

A Low Computational Cost Lightning Mapping Algorithm With a Nonuniform L-Shaped Array: Principle and Verification

Recently, 2-D multiple signal classification (MUSIC) algorithm was applied to map lighting propagation with improved quality. However, the quality is improved at the cost of increasing the calculation load. To improve the efficiency, this article proposes an improved MUSIC-based lightning mapping algorithm, which transforms the 2-D direction of arrival (DOA) estimation problem for an L-shaped array into 1-D DOA estimation problem for the two arms of the array. Additionally, the lightning mapping array is arranged in a nonuniform way to reduce the risk of trivial ambiguity problem. The performance of the developed lightning mapping system is verified with numerical Monte Carlo simulation and further with real lightning observation results. The simulation results show that the nonuniform array outperforms the uniform array in terms of detection accuracy, spatial resolution, and antiambiguity. The observation results show that the MUSIC-based methods produce better lightning mapping quality compared with the interferometry method (15 777 sources mapped), due to better robustness for low signal-to-noise ratio (SNR) lightning very high-frequency (VHF) signals. Compared with the 2-D MUSIC method (34 620 sources mapped), the proposed low computational cost method (30 461 sources mapped) is proven to produce fairly similar path of lightning leader with significant lower computational cost. Specifically, the proposed method saves about 84.91% of calculation time compared with the 2-D MUSIC method in practice.

Quantum chemical calculation of normal vibration frequencies of polyatomic molecules

Quantum calculation of molecular vibrational frequency is important in investigating infrared spectrum and Raman spectrum. In this work, a low computational cost method of calculating the quantum chemistry of vibrational frequencies for large molecules is proposed. Usually, the calculation of vibrational frequency of a molecule containing <i>N</i> atoms needs to deal with the Hessian matrix, which consists of second derivatives of the 3<i>N</i>-dimensional potential hypersurface, and then solve secular equations of the matrix to obtain normal vibration modes and the corresponding frequencies. Larger <i>N</i> implies higher computational cost. Therefore, for a limited computational hardware condition, higher-level computations for large <i>N</i> atomic molecule’s vibrational frequencies cannot be implemented in practice. Here we solve this problem by calculating the vibrational frequency for only one vibrational mode each time instead of calculating the Hessian matrix to obtain all vibrational frequencies. When only one vibrational mode is taken into consideration, the molecular potential hypersurface can be transformed into one-dimensional curve. Hence, we can calculate the curve with high-level computational method, then deduce the expression of one-dimensional curve by using harmonic oscillating approximation and obtain the vibrational frequency by using the expression to fit the curve. It should be noted that this method is applied to vibrational modes whose vibrational coordinates can be completely determined by equilibrium geometry and the molecular symmetry and be independent of the molecular force constants. It requires that there exists no other vibrational mode with the same symmetry but with different frequencies. The lower computational cost for a one-dimensional potential curve than that for 3<i>N</i>-dimensional potential hypersurface’s second derivatives permits us to use higher-level method and larger basis set for a given computational hardware condition to achieve more accurate results. In this paper we take the calculation of<i> B</i><sub>2</sub> vibrational frequency of water molecule for example to illustrate the feasibility of this method. Furthermore, we use this method to deal with the SF<sub>6</sub> molecule. It has 7 atoms and 70 electrons, hence there exists a large amount of electronic correlation energy to be calculated. The MRCI is an effective method to calculate the correlation energy. But by now no MRCI result of SF<sub>6</sub> vibrational frequencies has been reported. So here we use MRCI/6-311G* to calculate the potential curves of A<sub>1g</sub>, E<sub>g</sub>, T<sub>2g</sub> and T<sub>2u</sub> vibrational modes separately, deduce their expressions, then use the expressions to fit the curves, and finally obtain the vibrational frequencies. The results are then compared with those obtained by other theoretical methods including HF, MP2, CISD, CCSD(T) and B3LYP methods through using the same 6-311G* basis set. It is shown that the relative error to experimental result of the MRCI method is the least in the results from all these methods.

Ensemble Density Functional Theory of Neutral and Charged Excitations : Exact Formulations, Standard Approximations, and Open Questions.

Recent progress in the field of (time-independent) ensemble density-functional theory (DFT) for excited states are reviewed. Both Gross-Oliveira-Kohn (GOK) and N-centered ensemble formalisms, which are mathematically very similar and allow for an in-principle-exact description of neutral and charged electronic excitations, respectively, are discussed. Key exact results, for example, the equivalence between the infamous derivative discontinuity problem and the description of weight dependencies in the ensemble exchange-correlation density functional, are highlighted. The variational evaluation of orbital-dependent ensemble Hartree-exchange (Hx) energies is discussed in detail. We show in passing that state-averaging individual exact Hx energies can lead to severe (although solvable) v-representability issues. Finally, we explore the possibility of using the concept of density-driven correlation, which has been introduced recently and does not exist in regular ground-state DFT, for improving state-of-the-art correlation density-functional approximations for ensembles. The present review reflects the efforts of a growing community to turn ensemble DFT into a rigorous and reliable low-cost computational method for excited states. We hope that, in the near future, this contribution will stimulate new formal and practical developments in the field.

Electronic transport through a biphenyl system as a function of torsion angle with a complex absorbing potential to model the self-energy in a scattering approach

In this work, we present a self-energy model based on the complex absorbing potential (CAP) method to calculate the transmission function through an extended molecule using scattering theory. Once the CAP mimics an infinite environment at the ends of a finite system, it can be used as a model for self-energy with a low computational cost. Moreover, the matrixes required for the transport calculation can be obtained from an ab initio calculation of some extended molecules in a single step using an adjustable model, thus taking into account changes in the electronic structure of the system. This approach was applied to study electron transport across a biphenyl molecular system for different torsion angles under an external applied electric field. The results obtained are in good agreement with the available theoretical and experimental results in the literature and provide an efficient approach, with a low computational cost method, for the interpretation of electrical transport at the molecular level.

Mathematical model-based redesign of chickpea harvester reel

Aim of study: This paper presents a mathematical modeling approach to redesign the reels of chickpea harvesters for harvest efficiency.Area of study: A prototype chickpea harvester was designed and evaluated on the Dooshan farm of the University of Kurdistan, Sanandaj, Iran.Material and methods: The strategy used for reducing harvesting losses derived from the dynamic study of the reel applied to the chickpea harvester. The machine was designed such that bats of a power take-off (PTO)-powered reel, in conjunction with passive fingers, harvest pods from anchored plants and throw the pods into a hopper. The trochoid trajectory of the reel bats concerning reel kinematic index, and plant height and spacing was determined for redesigning the reel.Main results: This kinematic design allowed an estimation of the reel orientation at the time of impact. The experimentally validated model offers an accurate and low computational cost method to redesign harvester reels.Research highlights: The new chickpea harvester implemented with a four fixed-bat reel, a height of 40 cm above the ground for the reel axis, and featuring a kinematic index of 2.4 was capable of harvesting pods with harvesting efficiency of over 70%; a significant improvement in harvesting performance.

Seismic performance assessment of tunnel form concrete structures under earthquake sequences using endurance time analysis

A low-computational cost method is developed for seismic performance assessment of tunnel-form concrete structures under earthquake sequences. Using incremental dynamic analysis (IDA), a structure already damaged under a code spectrum-compatible mainshock is subjected to a set of aftershock events. The seismic reliability of the structure under successive earthquakes is then evaluated by comparing the fragility curves of the intact and the damaged structures. To achieve this, the newly developed endurance time approach is used to obtain the residual capacity of the structures to withstand aftershocks. The efficiency of the proposed method is demonstrated by using 5 and 10-storey 3D tunnel-form concrete structures as case study examples. It is shown that using endurance time method for seismic performance evaluation under mainshock-aftershock sequences generally leads to accurate results (less than 15% error), while it can significantly reduce (up to 90%) the computational costs of the conventional IDA method. The results also indicate that the application of the code design basis earthquake as the mainshock has no significant effect on the seismic performance of the tunnel form concrete structures under aftershocks, especially for the shorter systems. This highlights the excellent seismic resistance of tunnel-form structures under earthquake sequences.

An Efficient Low Computational Cost Method of R-Peak Detection

Visual inspection of R-peaks in an electrocardiogram (ECG) signal may lead to wrong diagnosis due to physiological variability and the noisy status of the QRS complexes causing its incorrect interpretation. Hence, computer-aided diagnosis (CAD) is required for better and correct diagnosis of cardiovascular diseases and interpretation of essential clinical information. Low computational cost CAD systems with good accuracy are always preferred in health informatics. Presently, performance of most of the ECG signal analysis techniques such as feature extraction, classification etc. depends heavily on the used pre-processing technique, which may consume appreciable portion of the total processing time. Therefore, it can be inferred that if pre-processing technique is skipped altogether without compromising the detection accuracy of a diagnosis, then it would save central processing unit (CPU) time reducing the overall computational time. This time saving will turn out to be very important in critical and emergency situations to save lives of several patients. Hence, the authors were motivated to propose an efficient low computational cost method based on fractional Fourier transform (FrFT) as a feature extraction technique eliminating the need of any pre-processing. In other words, the proposed technique is applied directly on the raw ECG data resulting in computational savings. Firstly, eigenvalues and eigenfunctions are proposed to be obtained using FrFT. And secondly, these are proposed to be utilized for R-peak detection using Independent Principal Component Analysis (IPCA) on the basis of kurtosis and variance of the extracted features in a noisy ECG signal with different morphologies. The proposed methodology is evaluated on the basis of various performance parameters viz. sensitivity (SE), accuracy (ACC), and positive predictive value (PPV) (of the detected ECG beats). SE of 99.97%, PPV of 99.98%, and ACC of 99.94% are obtained on MIT-BIH Arrhythmia (MIT-BIH Arr) database. All simulations are done using Intel Core i3-3240 Dual-Core Processor 3.4 GHz and 8 GB of RAM using MATLAB R2011a. The average processing time of CPU is observed to be 0.677 s with detection error rate (DER) of 0.058%. Both these values are least among other existing techniques, which establish that the proposed method incurs low computational cost. Also, consistently high values of all the performance parameters such as SE, PPV and ACC demonstrates the robustness of the proposed technique. Hence, the proposed methodology is expected to assist cardiologists for intelligent, effective, and timely diagnosis of heart rhythm irregularities.

A new release of MOPAC incorporating the INDO/S semiempirical model with CI excited states.

The semiempirical INDO/S Hamiltonian is incorporated into a new release of MOPAC2016. The MOPAC2016 software package has long been at the forefront of semiempirical quantum chemical methods (SEQMs) for small molecules, proteins, and solids and until this release has included only NDDO-type SEQMs. The new code enables the calculation of excited states using the INDO/S Hamiltonian combined with a configuration interaction (CI) approach using single excitations (CIS), single and double excitations (CISD), or multiple reference determinants (MRCI) where reference determinants are generated using a complete active space (CAS) approach. The capacity to perform excited-state calculations beyond the CIS level makes INDO/CI one of the few low-cost computational methods capable of accurately modeling states with substantial double-excitation character. Solvent corrections to the ground-state and excited-state energies can be computed using the COSMO implicit solvent model, incorporating state-specific corrections to the excited states based on the solvent refractive index. This code produces physically reasonable electronic structures, absorption spectra, and solvatochromic shifts at low computational costs for systems up to hundreds of atoms, and for both organic molecules and metal clusters.

Multi-level Response Modification Factor Estimation for Steel Moment-Resisting Frames Using Endurance-Time Method

ABSTRACT The computational complexity and high computational costs are the main obstacles to estimate the seismic demand and capacity of non-linear structural systems and assess their seismic performance under strong earthquake events. On the other hand, the current response modification factors for seismic design of structures are mainly based on a single performance target, and therefore, cannot be directly used in modern performance-based design procedures. In this study, a low computational cost method is adopted for seismic performance assessment of steel moment-resisting frames and development of multi-level modification factors by using the newly-developed Endurance-Time method. The efficiency of the proposed method is demonstrated for 3, 5, and 7-storey frame examples. It is shown that compared to conventional methods using time-history and pushover analysis techniques, the proposed approach can significantly (up to 80%) reduce the computational costs. The results indicate that push-over analyses may lead to inaccurate results, while a good agreement is generally observed between the seismic demands predicted by Endurance-Time and Time-History methods (less than 10% error). The results of Endurance-Time method are then used to develop multi-level response modification factors for steel moment-resisting frames by taking into account the effects of earthquake intensity and the target performance level. It is shown that Endurance-Time method can accurately estimate the response modification factors and the corresponding earthquake intensity levels with significantly less computational cost compared to the conventional methods using incremental dynamic analysis. The proposed multi-level response modification factors should prove useful in performance-based seismic design of steel moment-resisting frames.

On the Selection of the Regularization Parameter in Stacking

Stacking is a model combination technique to improve prediction accuracy. Regularization is usually necessary in stacking because some predictions used in the model combination provide similar predictions. Cross-validation is generally used to select the regularization parameter, but it incurs a high computational cost. This paper proposes two simple low computational cost methods for selecting the regularization parameter. The effectiveness of the methods is examined in numerical experiments. Asymptotic results in a particular setting are also shown.

Multiagent Consensus Equilibrium in Molecular Structure Determination.

Multiagent consensus equilibrium (MACE) is demonstrated for the integration of experimental observables as constraints in molecular structure determination and for the systematic merging of multiple computational architectures. MACE is founded on simultaneously determining the equilibrium point between multiple experimental and/or computational agents; the returned state description (e.g., atomic coordinates for molecular structure) represents the intersection of each manifold and is not equivalent to the average optimum state for each agent. The moment of inertia, determined directly from microwave spectroscopy measurements, serves to illustrate the mechanism through which MACE evaluations merge experimental and quantum chemical modeling. MACE results reported combine gradient descent optimization of each ab initio agent with an agent that predicts the chemical structure based on root-mean-square deviation of the predicted inertia tensor with experimentally measured moments of inertia. Successful model fusion for several small molecules was achieved as well as the larger molecule solketal. Fusing a model of moment of inertia, an underdetermined predictor of structure, with low cost computational methods yielded structure determination performance comparable to standard computational methods such as MP2/cc-pVTZ and greater agreement with experimental observables.

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

Event-related potentials (ERPs) are important neurophysiological markers widely used in scientific, medical and engineering contexts. Proper ERP detection contributes to widening the scope of use and, in general, improving functionality. The morphology and latency of ERPs are variable among subject sessions, which complicates their detection. Although variability is an intrinsic feature of neuronal activity, it can be addressed with novel views on ERP detection techniques. In this paper, we propose an agile method for characterizing and thus detecting variable ERPs, which keeps track of their temporal and spatial information through the continuous measurement of the area under the curve in ERP components. We illustrate the usefulness of the proposed ERP characterization for electrode selection in brain-computer interfaces (BCIs) and compare the results with other standard methods. We assess ERP classification for BCI use with Bayesian linear discriminant analysis (BLDA) and cross-validation. We also evaluate performance with both the information transfer rate and BCI utility. The results of our validation tests show that this characterization helps to take advantage of the information on the evolution of positive and negative ERP components and, therefore, to efficiently select electrodes for optimized ERP detection. The proposed method improves the classification accuracy and bitrate of all sets of electrodes analyzed. Furthermore, the method is robust between different day sessions. Our work contributes to the efficient detection of ERPs, manages inter- and intrasubject variability, decreases the computational cost of classic detection methods and contributes to promoting low-cost personalized brain-computer interfaces.