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How homochirality concerning biopolymers (DNA/RNA/proteins) could have originally occurred (i.e., arisen from a non-life chemical world, which tended to be chirality-symmetric) is a long-standing scientific puzzle. For many years, people have focused on exploring plausible physic-chemical mechanisms that may have led to prebiotic environments biased to one chiral type of monomers (e.g., D-nucleotides against L-nucleotides; L-amino-acids against D-amino-acids)–which should have then assembled into corresponding polymers with homochirality, but as yet have achieved no convincing advance. Here we show, by computer simulation–with a model based on the RNA world scenario, that the biased-chirality may have been established at polymer level instead, just deriving from a racemic mixture of monomers (i.e., equally with the two chiral types). In other words, the results suggest that the homochirality may have originated along with the advent of biopolymers during the origin of life, rather than somehow at the level of monomers before the origin of life. Author summary: People have long been curious about the fact that central molecules in the living world (biopolymers), i.e., nucleic acids and proteins, are asymmetric in chirality (handedness), but as the relevant background, the chemical world is symmetric in chirality. Now that life should have originated from a prebiotic non-life background, how could this dissymmetry have occurred? Previous studies in this area focused their efforts on how the chirality-symmetry may have been broken at the monomer level (i.e., nucleotides or amino acids), but have achieved little advance over decades of years. Here we demonstrate, by in silico simulation, that instead, the required chirality-deviation may have been established along with the emergence of biopolymers at the beginning stage in the origin of life–just deriving from a chirality-symmetric monomer pool. The process is actually not only an issue of chemistry but also an issue involving evolution–thus previously difficult to reveal by pure lab work in this area. By modelling the evolutionary process, the present computer simulation study provides significant clues for experiments in future. [ABSTRACT FROM AUTHOR]

The High-degree Cubature Kalman Filter (HCKF) is proposed as a novel methodology based on the arbitrary degree spherical rule, which can achieve better performance than the traditional Kalman filter. However, it also has a large calculation burden when used in a high-dimension and high-degree of accuracy estimation system. The number of sampling points of an HCKF increases polynomially with increasing state-space dimensions, which further increases the calculation burden. The reduction of the number of the state-space dimensions is the main contribution of this study. A strategy for HCKF based on the partitioning of the state-space and orthogonal principle is introduced, referred to as the Multiple Robust HCKF (MRHCKF). It is shown that this technique can effectively reduce the calculation burden for the high-dimension system with robust performance. Numerical simulations are performed for the example of high-dimension relative position and attitude estimation to show that the proposed method can obtain nearly the same performance as the HCKF, while drastically reducing computational complexity. [ABSTRACT FROM AUTHOR]

Sparse adaptive filtering algorithms are utilized to exploit system sparsity as well as to mitigate interferences in many applications such as channel estimation and system identification. In order to improve the robustness of the sparse adaptive filtering, a novel adaptive filter is developed in this work by incorporating a correntropy-induced metric (CIM) constraint into the least logarithmic absolute difference (LLAD) algorithm. The CIM as an $$l_{0}$$ -norm approximation exerts a zero attraction, and hence, the LLAD algorithm performs well with robustness against impulsive noises. Numerical simulation results show that the proposed algorithm may achieve much better performance than other robust and sparse adaptive filtering algorithms such as the least mean p-power algorithm with $$l_{1}$$ -norm or reweighted $$l_{1}$$ -norm constraints. [ABSTRACT FROM AUTHOR]

How functional peptides may have arisen is a significant problem for the scenario of the RNA world. An attractive idea, the direct RNA template (DRT) hypothesis, proposes that RNA molecules can bind amino acids specifically and promote the synthesis of corresponding peptides, thereby starting the RNA/peptides world. To investigate the plausibility of this idea, we modeled the emergence of a "membrane-stabilizing peptide" in RNA-based protocells—such a peptide was suggested to have appeared early in the RNA world based on experimental evidence. The computer simulation demonstrated that the protocells containing the "RNA gene" encoding this peptide may spread in the system owing to the peptide's function. The RNA gene may either originate de novo in protocells or emerge in protocells already containing ribozymes—here we adopt a nucleotide synthetase ribozyme as an example. Furthermore, interestingly, we show that a "nucleotide synthetase peptide" encoded by RNA (also via the DRT mechanism) may substitute the nucleotide synthetase ribozyme in evolution, which may represent how "functional-takeover" in the RNA world could have occurred. Overall, we conclude that the transition from the RNA world towards an RNA/peptides world may well have been mediated by the DRT mechanism. Remarkably, the successful modeling on the emergence of membrane-stabilizing peptide in RNA-based protocells is per se significant, which may imply a "promising" way for peptides to enter the RNA world, especially considering the weak interaction between RNA and the membrane in chemistry. [ABSTRACT FROM AUTHOR]

Abstract: A significant problem of the origin of life is the emergence of cellular self-replication. In the context of the “RNA world”, a crucial concern is how the RNA-based protocells could achieve the ability to produce their own membrane. Here we show, with the aid of a computer simulation, that for these protocells, there would be “immediately” a selection pressure for the emergence of a ribozyme synthesizing membrane components. The ribozyme would promote the enlargement of cellular space and favor the incoming (by permeation) of RNA''s precursors, thus benefit the replication of inner RNA, including itself. Via growth and division, protocells containing the ribozyme would achieve superiority and spread in the system, and meanwhile the ribozyme would spread in the system. The present work is inspiring because it suggests that the transition from molecular self-replication to cellular self-replication might have occurred naturally (and necessarily) in the origin of life, leading to the emergence of Darwinian evolution at the cellular level. [Copyright &y& Elsevier]

Abstract: The origin of life remains a highly speculative field, mainly due to the shortage of our knowledge on prebiotic chemistry and basic understanding on the essence of life. In this context, computer simulation is expected to play an important role. For instance, the scenario concerning the genesis of the widely accepted RNA World remains blurry, though we have gathered some circumstantial evidence and fragmented knowledge on several supposed stages, including formation of polynucleotides from a prebiotic nucleotide pool, emergence of RNA replicases (RNA molecules catalyzing their own replication), and evolution of RNA replicases. It is highly valuable to simulate the stages as a continuous process to evaluate the plausibility of the supposition and study the rules involved. Here we construct a computer simulation on the process using Monte Carlo method. It demonstrates that primordial RNA replicases may appear and spread in a nucleotide pool provided they could recognize their own sequence and their complements as catalytic targets, and then may evolve to more efficient RNA replicases. Apart from its indication on the genesis of the RNA World, the vivid simulation of emergence of the “first replicative molecules” and their subsequent evolution is impressive and may help to get insight into “how could self-replication and Darwinian evolution, two key features of life, emerge in a non-life background?” thus improve our understanding of “what is life” when studying origins of life. [Copyright &y& Elsevier]

The origin of life involved complicated evolutionary processes. Computer modeling is a promising way to reveal relevant mechanisms. However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-setting for the modeling. Thus, typically, the studies were conducted in a reverse way: the parameter-space was explored to find those parameter values "supporting" a hypothetical scene (that is, leaving the parameter-justification a later job when sufficient knowledge is available). Exploring the parameter-space manually is an arduous job (especially when the modeling becomes complicated) and additionally, difficult to characterize as regular "Methods" in a paper. Here we show that a machine-learning-like approach may be adopted, automatically optimizing the parameters. With this efficient parameter-exploring approach, the evolutionary modeling on the origin of life would become much more powerful. In particular, based on this, it is expected that more near-reality (complex) models could be introduced, and thereby theoretical research would be more tightly associated with experimental investigation in this field–hopefully leading to significant steps forward in respect to our understanding on the origin of life. Author summary: People have long been interested in the evolutionary processes through which life on our planet could have arisen from a non-life background. However, it seems that experimental studies in this field are proceeding slowly, perhaps owing to the complication of such processes. In the meantime, computer modeling has shown its potential to disclose the evolutionary mechanisms involved. Now a major difficulty of the computer modeling work is to justify the parameter-setting–on account of our limited knowledge on prebiotic chemistry and environments. Thus, people tend to explore the parameter space to seek parameter values in favor of the hypothetic scene and leave the parameter-justification a later job when sufficient knowledge is available. To date, the parameter-exploration is usually conducted manually (in many cases by trial and error), thus arduous and unpredictable. Inspired by the algorithm of machine-learning, we designed an automatic approach of parameter-exploration. The results showed that the approach is quite effective–that is, "good" parameter-sets in favor of hypothetic scenes in the origin of life can be found automatically. It is expected that such a machine-learning-like method would greatly enhance the efficiency of our evolutionary modeling studies on the origin of life in future. [ABSTRACT FROM AUTHOR]

Corrosion defects can significantly reduce the ultimate bearing capacity of thick-walled pipelines in deep water. Uneven depth defects pose a serious threat to the integrity of pipeline structures. However, in the study of uneven depth defects, the influence of chord-cut defects is often overlooked. To study the impact of chord-cut corrosion defect on pipeline collapse response, a dynamic model is established. Two sets of full-scale pipeline experiments are conducted to verify the model. The validated model is used to obtain the pipeline collapse responses under different defect parameters, which include defect length, defect depth, defects' location, and pipeline ovality. The changes in pipeline collapse pressure and collapse mode under different defect parameters are analyzed. The research shows the increase in defect length and the decrease in diameter-to-thickness ratio (D/t) will enhance the influence of defect angle θ on pipeline collapse configuration. And the change of collapse mode will lead to abnormal changes in collapse pressure. With the increase of internal defect length, the collapse mode variation rule is "∞" - "∩" - "P" - "U". A collapse pressure empirical formula for pipeline with chord-cut corrosion defects is obtained. The empirical formula fits well with the numerical simulation results. • A 3D dynamic fluid cavity model for pipelines with chord-cut corrosion defects is proposed. • Two sets of full-scale experiments are conducted on pipelines with chord-cut corrosion defects. • The variation law of pipeline collapse mode under different external chord-cut defect angle θ are analyzed. • The influence of the size of the chord-cut defects on the mode and pressure of pipeline collapse is analyzed. • A collapse pressure empirical formula for pipeline with chord-cut corrosion defects is obtained. [ABSTRACT FROM AUTHOR]

The hot air anti-icing system is one of the important ways to prevent aircraft icing. Numerical simulation method was carried out to study the influence of the structural parameters on the thermal performance of the hot air anti-icing system. The hot air flow and heat transfer in the anti-icing cavities with different nozzle-to-surface height, number of the nozzle rows and hot air jet angle were simulated using the computational fluid dynamics (CFD) method. The thermal efficiency of the anti-icing system and the convective heat transfer coefficient distribution of the skin inner surface were obtained. The results show that; 1) with the increase of the nozzle-to-surface height in the range of 4mm to 36mm, the thermal efficiency of the anti-icing system reduces and the heat transfer performance of the hot air impinging on the surface weakens, 2) with consistent flow flux, the thermal efficiency of the anti-icing system increases from three to two nozzle rows, 3) the thermal efficiency improves with the 15° rotation of the hot air jet angle along the upper surface direction, indicating that the surface curvature effects the jet surface heat transfer performance. The results presented in this paper can help to the design of the anti-icing cavity. [ABSTRACT FROM AUTHOR]