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A low complexity scheme is developed to design the sparse linear phase FIR filters based on the improved iteratively re-weighted orthogonal matching pursuit(OMP) algorithm. The scheme employs the Frobenius norm to simplify searching for the desired sparse FIR filter at each iteration. Furthermore, a log function is introduced to enhance the weight updating process at each iteration. The simulation results demonstrate that our scheme can effectively generate the sparse linear phase FIR filters which satisfy the various given design specifications. [ABSTRACT FROM AUTHOR]

The fast, reliable, and accurate identification of IDPRs is essential, as in recent years it has come to be recognized more and more that IDPRs have a wide impact on many important physiological processes, such as molecular recognition and molecular assembly, the regulation of transcription and translation, protein phosphorylation, cellular signal transduction, etc. For the sake of cost-effectiveness, it is imperative to develop computational approaches for identifying IDPRs. In this study, a deep neural structure where a variant VGG19 is situated between two MLP networks is developed for identifying IDPRs. Furthermore, for the first time, three novel sequence features—i.e., persistent entropy and the probabilities associated with two and three consecutive amino acids of the protein sequence—are introduced for identifying IDPRs. The simulation results show that our neural structure either performs considerably better than other known methods or, when relying on a much smaller training set, attains a similar performance. Our deep neural structure, which exploits the VGG19 structure, is effective for identifying IDPRs. Furthermore, three novel sequence features—i.e., the persistent entropy and the probabilities associated with two and three consecutive amino acids of the protein sequence—could be used as valuable sequence features in the further development of identifying IDPRs. [ABSTRACT FROM AUTHOR]

In this paper, we simulated the decomposition mechanism of 3-methyl-2,6-dinitrophenol (MDNP) based on reaction molecular dynamics using ReaxFF force field. In addition, the evolution of some main products over time at different heating rates (10, 15, and 20 K·ps−1) was studied. As indicated by the simulation results, with the elevation at different heating rates, the time required for the system to reach equilibrium was shortened, and more products were obtained. At three heating rates, C7H7O5N2, C7H6O4N2, C7H5O5N2, C7H5O4N2, HON, NO, and NO2 were the main intermediate products, and N2, CO2, H2O, H2, and NH3 were the primary final products. To be specific, C7H5O5N2 was the first produced intermediate product, and H2O was the first produced final product with the largest abundance. The intermediate products first increased and then decreased to zero. Moreover, the primary chemistry reactions in the MDNP pyrolysis were simulated through ReaxFF MD simulations. [ABSTRACT FROM AUTHOR]

Background: Molecular recognition features (MoRFs) are one important type of disordered segments that can promote specific protein-protein interactions. They are located within longer intrinsically disordered regions (IDRs), and undergo disorder-to-order transitions upon binding to their interaction partners. The functional importance of MoRFs and the limitation of experimental identification make it necessary to predict MoRFs accurately with computational methods. Results: In this study, a new sequence-based method, named as MoRFMPM, is proposed for predicting MoRFs. MoRFMPM uses minimax probability machine (MPM) to predict MoRFs based on 16 features and 3 different windows, which neither relying on other predictors nor calculating the properties of the surrounding regions of MoRFs separately. Comparing with ANCHOR, MoRFpred and MoRFCHiBi on the same test sets, MoRFMPM not only obtains higher AUC, but also obtains higher TPR at low FPR. Conclusions: The features used in MoRFMPM can effectively predict MoRFs, especially after preprocessing. Besides, MoRFMPM uses a linear classification algorithm and does not rely on results of other predictors which makes it accessible and repeatable. [ABSTRACT FROM AUTHOR]

Recently, chalcogenides have attracted much attention as electrocatalysts in hydrogen evolution reaction (HER). However, few studies have been conducted on the electrocatalytic properties of gallium oxides and chalcogenides. In this paper, a Ga 2 XY (X ≠ Y, X, Y=O, S, Se, Te) defect structure doped by non-metal B, C, N, P, Si, and As have been designed. According to the study, the doping of non-metal atoms can significantly enhance their HER properties, the Ga 2 OSe-As Xi -Xi structure and Ga 2 SeTe-Si Xi -NM structure possess intensely excellent HER properties in this study with the Gibbs free energy of 0.01 eV and 0.00 eV, respectively. It is found that the Ga 2 SeTe structure has a more concentrated electron transfer range compared to the Ga 2 OSe structure, leading to a superior HER performance. This work provides a new idea for the study of HER electrocatalytic performance of the Ga 2 XY system, and it is expected to be applied to HER catalysts affordably and efficiently. • Screening of non-metal doped Ga 2 XY defective structures for HER electrocatalytic performance. • The introduction of Ga vacancies significantly enhances the HER performance of Ga 2 XY structures. • The Ga 2 SeTe defective doped structure has a more concentrated electron transfer range compared to the Ga 2 OSe structure. • The As-doped Ga 2 OSe structure and the Si-doped Ga 2 SeTe structure containing Ga defect have a near-zero ΔG H. [ABSTRACT FROM AUTHOR]

We employ the Rayleigh entropy maximization to develop a novel IDP scheme which requires computing only five features for each residue of a protein sequence, that is, the Shannon entropy, topological entropy, and the weighted average values of three propensities. Furthermore, our scheme is a linear classification method and hence requires computing simpler decision curves which are more robust as well as using fewer learning samples to compute. The simulation results of our scheme as well as some existing schemes demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

The finite‐impulse‐response (FIR) decision feedback equalisers (DFEs) with a large number of taps are used to eliminate the intersymbol interference. In this Letter, a hybrid optimisation approach based on reweighted ℓ1‐norm minimisation and the greedy algorithm is proposed to get a better estimation of the non‐zero taps. First, the authors transform the problem of designing sparse FIR multiple‐input multiple‐output DFEs into an ℓ0‐norm minimisation problem, then use the proposed approach, which involves two stages as the preliminary selection of non‐zero tap positions and re‐optimisation with non‐zero taps, to determine the positions and values of the non‐zero taps for the FIR DFEs. The simulation results demonstrate the effectiveness of the proposed hybrid optimisation approach. [ABSTRACT FROM AUTHOR]

In this paper, a paradigm is developed to design the linear-phase FIR multiple-notch filters with variable notch frequencies. The design procedure can be proceeded through two steps: First, a linear-phase narrow-band low-pass filter met the given bandwidth and stopband ripple specifications is designed. Second, a tuning procedure is applied to the computed low-pass filter to yield the desired multiple-notch filter. When the notch frequencies are varied, the same tuning procedure can be employed to render the multiple-notch filter with the new set of the notch frequencies. The tuning procedure employed reduces the computational complexity of designing the multiple-notch filter with the new set of the notch frequencies. The simulation results demonstrate the effectiveness of our scheme. [ABSTRACT FROM AUTHOR]

The accurate of i identificationntrinsically disordered proteins or protein regions is of great importance, as they are involved in critical biological process and related to various human diseases. In this paper, we develop a deep neural network that is based on the well-known VGG16. Our deep neural network is then trained through using 1450 proteins from the dataset DIS1616 and the trained neural network is tested on the remaining 166 proteins. Our trained neural network is also tested on the blind test set R80 and MXD494 to further demonstrate the performance of our model. The M C C value of our trained deep neural network is 0.5132 on the test set DIS166, 0.5270 on the blind test set R80 and 0.4577 on the blind test set MXD494. All of these M C C values of our trained deep neural network exceed the corresponding values of existing prediction methods. [ABSTRACT FROM AUTHOR]

Hydrogen as a sustainable and clean energy source has been widely noticed, but it is urgent to find an efficient and stable electrocatalyst to increase its hydrogen production. For the catalytic performance of electrocatalysts, defects, doping, and stress are usually selected to enhance the catalytic performance, on the basis of which in this paper, MSi 2 N 4 (M = Mo, W) system containing vacancies and bimetallic doping was constructed and analyzed for its HER performance. The results show that the formation energy of all structures is stable and Ni doping can significantly improve the stability according to formation energy of the structures with vacancy. Both ipsilateral doping and contralateral doping can significantly improve the Gibbs free energy of MA 2 Z 4 materials, especially the MoSi 2 N 4 doped with double Co. In particular, the values of Gibbs free energy of C-Ni-V N @MoSi 2 N 4 -N in and C-Co-V N @WSi 2 N 4 -N in are −0.027 eV and 0.041 eV, significantly better than the performance of the recognized electrocatalyst Pt, which has a free energy of only 0.09 eV. After doped with transition metallic atoms, the energy bands of all materials present semiconductor property. Our work provides a novel method for the regulation of MA 2 Z 4 family materials and a theoretical strategy for designing efficient HER electrocatalysts. • Screening of bimetal-doped MA 2 Z 4 with defective structures for HER catalytic activity. • The HER performance of the ipsilateral doping is better than that of the contralateral doping. • The vacancies can significantly improve the formation energy of Ni doped materials. • The MoSi 2 N 4 defect structure doped with double Co has a near-zero ΔG H. [ABSTRACT FROM AUTHOR]

Molecular recognition features (MoRFs) are one important type of intrinsically disordered proteins functional regions that can undergo a disorder-to-order transition through binding to their interaction partners. Prediction of MoRFs is crucial, as the functions of MoRFs are associated with many diseases and can therefore become the potential drug targets. In this paper, a method of predicting MoRFs is developed based on the sequence properties and evolutionary information. To this end, we design two distinct multi-layer perceptron (MLP) neural networks and present a procedure to train them. We develop a preprocessing process which exploits different sizes of sliding windows to capture various properties related to MoRFs. We then use the Bayes rule together with the outputs of two trained MLP neural networks to predict MoRFs. In comparison to several state-of-the-art methods, the simulation results show that our method is competitive. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins perform a variety of important biological functions, which makes their accurate prediction useful for a wide range of applications. We develop a scheme for predicting intrinsically disordered proteins by employing 35 features including eight structural properties, seven physicochemical properties and 20 pieces of evolutionary information. In particular, the scheme includes a preprocessing procedure which greatly reduces the input features. Using two different windows, the preprocessed data containing not only the properties of the surroundings of the target residue but also the properties related to the specific target residue are fed into a multi-layer perceptron neural network as its inputs. The Adam algorithm for the back propagation together with the dropout algorithm to avoid overfitting are introduced during the training process. The training as well as testing our procedure is performed on the dataset DIS803 from a DisProt database. The simulation results show that the performance of our scheme is competitive in comparison with ESpritz and IsUnstruct. [ABSTRACT FROM AUTHOR]

Given the elevated expense and constrained accessibility of platinum, the primary catalyst employed in electrocatalytic hydrogen evolution, it becomes essential to pinpoint an alternative catalyst demonstrating exceptional catalytic efficacy and broader scalability potential. In this study, we undertook an exhaustive inquiry into the electrocatalytic activity concerning hydrogen evolution of the monolayer Janus MoXTe (where X represents S and Se) utilizing first-principle calculation. Furthermore, an evaluation of the performance of defective MoXTe structures was conducted. Our findings illuminate that the incorporation of non-metallic elements like B, C, N, and P into MoXTe can be employed to finely adjust the Gibbs free energy (ΔG H) to approximately 0 eV. Particularly, the doping of B and P into the 1T phase of MoSTe (1T-MoSTe-B@ represents 1T phase of MoSTe-B@ and 1T-MoSTe-P@ represents 1T phase of MoSTe-P@), the doping of P into the 1T′ phase of MoSTe (1T′ phase of MoSTe-P@), and the doping of P into the 1T′ phase of MoSeTe (1T′ phase of MoSeTe-P@) exhibit ΔG H values that are in close proximity to zero (ΔG H = 0.02, −0.03, 0.06, and −0.14 eV, respectively). Through further analysis, we found that the pristine and defective structures all exhibit metallic properties. And the doping of P improved HER performance more effectively among four nonmetallic dopants. In details, H get 0.21, 0.29, 0.43 and 0.28 e on doping of B and P into 1T phase MoSTe (1T phase of MoSTe-B@ and 1T phase of MoSTe-P@) and the doping of P into 1T′ phase MoSeTe (1T' phase of MoSeTe-P@). This study provides strategies for the design of MoXTe monolayer electrocatalysts, and it is expected to be applied to HER catalysts in affordable and efficient manner. • Screening of Catalytic Activity of 1T, and 1T′ phases Janus MoXTe (X = S, Se) monolayers. • Nonmetallic atom doping can affect the interaction between H and the material surface. • The doping of B and P into the 1T phase of MoSTe, P into the 1T′ phase of MoSTe and MoSeTe can improve the conductivity. [ABSTRACT FROM AUTHOR]

In this paper, a novel scheme using hybrid l1/l2 norm minimization and the orthogonal matching pursuit (OMP) algorithm is proposed to design the sparse finite impulse response (FIR) decision feedback equalizers (DFE) in multiple input multiple output (MIMO) systems. To reduce the number of nonzero taps for the FIR DFE while ensuring its design accuracy, the problem of designing a sparse FIR DFE is transformed into an l0 norm minimization problem, and then the proposed scheme is used to obtain the sparse solution. In the proposed scheme, a sequence of minimum weighted l2 norm problems is solved using the OMP algorithm. The nonzero taps positions can be corrected with the different weights in the diagonal weighting matrix which is computed through the hybrid l1/l2 norm minimization. The simulation results verify that the sparse FIR MIMO DFEs designed by the proposed scheme get a significant reduction in the number of nonzero taps with a small performance loss compared to the non-sparse optimum DFE under the minimum mean square error (MMSE) criterion. In addition, the proposed scheme provides better design accuracy than the OMP algorithm with the same sparsity level. [ABSTRACT FROM AUTHOR]

In an effort to reduce macroparticle defects in arc ion plating, a hollow cathode vacuum arc was added to an arc ion plating deposition chamber, so that the discharge from the hollow cathode vacuum arc was between the target and the substrate. AlTiN coatings were deposited using this technique (hollow cathode vacuum arc enhanced arc ion plating or HCVA-AIP) on high-speed steel substrates, and analyzed using scanning electron microscopy and X-ray diffraction. The hollow cathode vacuum arc was found to reduce the number and size of macroparticles in the deposited coatings and to increase the deposition rate. The hollow cathode vacuum arc also reduces the arc ion plating target voltage, by up to 29% (to 12 V) under the conditions studied here. Using this technique, the rate of Al ionization in the target is enhanced, resulting in the formation of AlN in the coating. This effect is strengthened at a higher hollow cathode vacuum arc discharge current. [ABSTRACT FROM AUTHOR]

The low efficiency of growing single-walled carbon nanotubes (SWCNT) poses a barrier to their application in high-performance electronic devices. However, it is difficult to control the uniform growth of SWCNT in a floating catalytic reactor due to the complex parameter control. Therefore, it is essential to enhance the growth of SWCNT in the floating catalyst chemical vapor deposition (FCCVD) process. In the present work, the influence of the reactive flow field on the growth of SWCNT, which is often neglected, is revealed. To address this issue, this work combines experiments and simulations to obtain the characteristics of the field distribution within the reactor and the trend of the products. The results of the flow field analysis indicate that thermal buoyancy is the cause of SWCNT growth limitation in FCCVD. By weakening the thermal buoyancy, a homogeneous reaction field is obtained; vortices in the flow field are reduced or even disappear; the temperature field is more homogeneous, and, importantly, the crystallinity of SWCNT is enhanced (I G /I D up to 20-fold). In addition, the decomposition process of the carbon source is also enhanced, thus suppressing the generation of by-products. Based on the results of the small tube experiments, both the increase in temperature and the decrease in residence time increased the I G /I D. Furthermore, the distributions of the maximum and minimum diameters in SWCNT imply variations in the growth modes of SWCNT at different temperatures. [Display omitted] • CFD was used to simulate the flow distribution inside the reactor. • The quality of SWCNT was improved using a simple method in the FCCVD process. • The time-dependent and temperature-dependent growth of SWCNT was investigated. [ABSTRACT FROM AUTHOR]

We focus on the design of finite impulse response (FIR) multiple notch filters. To reduce the computational complexity and hardware implementation complexity, a novel algorithm is developed based on the mixture of the tuning of notch frequencies and the sparsity of filter coefficients. The proposed design procedure can be carried out as follow: first, since sparse FIR filters have lower implementation complexity than full filters, a sparse linear phase FIR single notch filter with the given rejection bandwidth and passband attenuation is designed. Second, a tuning procedure is applied to the computed sparse filter to produce the desired sparse linear phase FIR multiple notch filter. When the notch frequencies are varied, the same tuning procedure can be employed to render the new multiple notch filter instead of designing the filter from scratch. The effectiveness of the proposed algorithm is demonstrated through three design examples. [ABSTRACT FROM AUTHOR]

The detection of short exons is a challenging open problem in the field of bioinformatics. Due to the fact that the weakness of existing model-independent methods lies in their inability to reliably detect small exons, a model-independent method based on the singularity detection with wavelet transform modulus maxima has been developed for detecting short coding sequences (exons) in eukaryotic DNA sequences. In the analysis of our method, the local maxima can capture and characterize singularities of short exons, which helps to yield significant patterns that are rarely observed with the traditional methods. In order to get some information about singularities on the differences between the exon signal and the background noise, the noise level is estimated by filtering the genomic sequence through a notch filter. Meanwhile, a fast method based on a piecewise cubic Hermite interpolating polynomial is applied to reconstruct the wavelet coefficients for improving the computational efficiency. In addition, the output measure of a paired-numerical representation calculated in both forward and reverse directions is used to incorporate a useful DNA structural property. The performances of our approach and other techniques are evaluated on two benchmark data sets. Experimental results demonstrate that the proposed method outperforms all assessed model-independent methods for detecting short exons in terms of evaluation metrics. [ABSTRACT FROM AUTHOR]

Cosine-modulated filter banks play a major role in digital signal processing. Sparse FIR filter banks have lower implementation complexity than full filter banks, while keeping a good performance level. This paper presents a fast design paradigm for sparse nearly perfect-reconstruction (NPR) cosine-modulated filter banks. First, an approximation function is introduced to reduce the non-convex quadratically constrained optimization problem to a linearly constrained optimization problem. Then, the desired sparse linear phase FIR prototype filter is derived through the orthogonal matching pursuit (OMP) performed under the weighted l 2 norm. The simulation results demonstrate that the proposed scheme is an effective paradigm to design sparse NPR cosine-modulated filter banks. [ABSTRACT FROM AUTHOR]