A domain decomposition strategy for alignment of multiple biological sequences on multiprocessor platforms

Multiple Sequences Alignment (MSA) of biological sequences is a fundamental problem in computational biology due to its critical significance in wide ranging applications including haplotype reconstruction, sequence homology, phylogenetic analysis, and prediction of evolutionary origins. The MSA problem is considered NP-hard and known heuristics for the problem do not scale well with increasing numbers of sequences. On the other hand, with the advent of a new breed of fast sequencing techniques it is now possible to generate thousands of sequences very quickly. For rapid sequence analysis, it is therefore desirable to develop fast MSA algorithms that scale well with an increase in the dataset size. In this paper, we present a novel domain decomposition based technique to solve the MSA problem on multiprocessing platforms. The domain decomposition based technique, in addition to yielding better quality, gives enormous advantages in terms of execution time and memory requirements. The proposed strategy allows one to decrease the time complexity of any known heuristic of O(N)^x$O{\left(N\right)}^x$ complexity by a factor of O(1/p)^x$O{(1/p)}^x$, where N$N$ is the number of sequences, x$x$ depends on the underlying heuristic approach, and p$p$ is the number of processing nodes. In particular, we propose a highly scalable algorithm, Sample-Align-D, for aligning biological sequences using Muscle system as the underlying heuristic. The proposed algorithm has been implemented on a cluster of workstations using the MPI library. Experimental results for different problem sizes are analyzed in terms of quality of alignment, execution time and speed-up.     

Deep learning methods for fingerprint-based indoor positioning: a review

ABSTRACT

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.     

Sequency Domain Signal Processing Using Complex Hadamard Transform

Signal processing can be performed in time domain as well as intransform domain. Frequency domain is perhaps the most widely used domain for transform domain signal processing. In this paper we analyze sequency domain (SD) signal processing as an alternate to the conventional frequency domain signal processing. We perform signal processing in sequency domain using conjugate-symmetric sequency-ordered complex Hadamard transform and compare the results with those of discrete Fourier transform. We observe that in comparison with frequency spectrum, spectral energy of sequency spectrum is spread over the entire spectrum. We performed signal and image denoising in SD and found that our designed SD filters effectively denoise the signals.     

汽车安全集成

本文探讨了主动安全的特点、分类及发展动向.     

Novel inorganic precursors [Co4.32Zn1.68(HCO2)18(C2H8N)6]/SiO2 and Co4.32Zn1.68(HCO2)18(C2H8N)6]/Al2O3 for Fischer–Tropsch synthesis

The silica- and alumina-supported Co–Zn catalysts were synthesized by thermal decomposition of new inorganic precursors [Co_4.32Zn_1.68(HCO₂)₁₈(C₂H₈N)₆]/SiO₂ or Al₂O₃. A novel coordination polymer formulated as [Co_4.32Zn_1.68(HCO₂)₁₈(C₂H₈N)₆] (1) was prepared using the solvothermal technique and characterized by elemental analysis, FT-infrared spectroscopy. Thermal stability of the complex 1 was investigated by thermogravimetric analysis and differential scanning calorimetry, and its structure was determined by single-crystal X-ray diffraction. Characterization of catalysts was carried out using powder X-ray diffraction (XRD), scanning electron microscopy (SEM), and BET specific surface area. The catalysts were evaluated for Fischer–Tropsch synthesis (FTS) in the temperature range 200–300 °C. The results revealed that the synthesized catalysts have higher selectivity to the desired products at 260 °C. The performance of the catalysts was compared to those of catalysts constructed via impregnation method and the fabricated catalysts show higher activity and selectivity than the reference catalysts.     

FexCo1−x-doped titanium oxide nanotubes as effective photocatalysts for hydrogen extraction from ammonium phosphate

Theoretically, tri-ammonium phosphate (NH₄)₃PO₄ embeds considerable amount of hydrogen. Typically, the expected hydrogen release from this cheap and stable material is 73.83 mmol/g_salt if a proper catalyst is exploited in the hydrolysis reaction. In this study Fe_xCo_1?x-doped titanium oxide nanotubes are introduced as an efficient photocatalyst under solar radiation. The introduced modified titanium oxide nanotubes have been prepared in two successive steps. First, Na-doped TiO₂ nanotubes were synthesized by hydrothermal treatment in presence of 10 N NaOH solution at 160 °C for 16 h. Then, doping by the proposed metals was carried out by ion exchange process in a microwave oven. X-ray photoelectron microscopy (XPS) and transmission electron microscopy (TEM) confirmed the success of the doping process and the nanotubular morphology, respectively. Study the photo characteristics indicated that the proposed metal doping shifted the band gap from UV to the visible light region as the estimated band gap energies for the as-prepared and doped nanotubes were 3.4 and 2.1 eV, respectively. Moreover, distinct enhancement for the visible light absorption capacity was observed. Accordingly, a distinguished improvement in the photocatalytic activity toward tri-ammonium phosphate hydrolysis was observed. However, the two metals content has a strong influence on the amount of the obtained hydrogen per gram of tri-ammonium phosphate salt. Numerically, the maximum obtained hydrogen was 4.0, 11.2, 11.2, 11.6, 13.4, 16.5, 17.4, 13.4 and 9.8 mmol/g_salt for the pristine TiO₂, and Fe_xCo_1?x-doped TiO₂ with x = 1, 0.8, 0.6, 0.5, 0.4, 0.3, 0.2, and 0.0, respectively.