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We develop computational mechanical modeling and methods for the analysis and simulation of the motions of a human body. This type of work is crucial in many aspects of human life, ranging from comfort in riding, the motion of aged persons, sports performance and injuries, and many ergonomic issues. A prevailing approach for human motion studies is through lumped parameter models containing discrete masses for the parts of the human body with empirically determined spring, mass, damping coefficients. Such models have been effective to some extent; however, a much more faithful modeling method is to model the human body as it is, namely, as a continuum. We present this approach, and for comparison, we choose two digital CAD models of mannequins for a standing human body, one from the versatile software package LS‐DYNA and another from open resources with some of our own adaptations. Our basic view in this paper is to regard human motion as a perturbation and vibration from an equilibrium position which is upright standing. A linear elastodynamic model is chosen for modal analysis, but a full nonlinear viscoelastoplastic extension is possible for full‐body simulation. The motion and vibration of these two mannequin models is analyzed by modal analysis, where the normal vibration modes are determined. LS‐DYNA is used as the supercomputing and simulation platform. Four sets of low‐frequency modes are tabulated, discussed, visualized, and compared. Higher frequency modes are also selectively displayed. We have found that these modes of motion and vibration form intrinsic basic modes of biomechanical motion of the human body. This view is supported by our finding of the upright walking motion as a low‐frequency mode in modal analysis. Such a "walking mode" shows the in‐phase and out‐of‐phase movements between the legs and arms on the left and right sides of a human body, implying that this walking motion is spontaneous, likely not requiring any directives from the brain. Dynamic motions of CAD mannequins are also simulated by drop tests for comparisons and the validity of the models is discussed through Fourier frequency analysis. All computed modes of motion are collected in several sets of video animations for ease of visualization. Samples of LS‐DYNA computer codes are also included for possible use by other researchers. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) is a problem of finding the most influential nodes with limited budgets under the given propagation model, which finally maximizes the spread of influence into the network. This work considers the robustness assumption in the IM problem, named the robust influence maximization (RIM) problem, which focuses on the uncertainty factors among the influence propagation models and algorithms, so as to find nodes that can achieve robust performance with different diffusion functions in networks. For modeling the uncertainty, we generate parameter space by adding noises into the independent cascade (IC) propagation model, in which each edge has an interval to deposit its all possible activation probabilities. Then, a novel robust optimal objective function is proposed for the RIM problem, which based on the calculation of the Shapley value , one classical and crucial concept in cooperative game theory. Particularly, to face the computing time explosion for traversing various uncertainty scenarios, we first give a complete parallel computing based framework to improve the proposed algorithm's efficiency, which will definitely motivate the research that how to fully use the computing resources to speed up the RIM problem solving. Through extensive experiments with nine real-world/synthetic networks and four centrality-based IC models, we find one exciting rule that the worst case on the perturbation interval model occurs when edge probability is equal to the endpoint of the interval, and the influence spread effect is positively correlated with the difference of probabilities between two directions over an edge. The seed nodes selected by the proposed robust algorithm outperforms other state-of-the-art methods under the worst diffusion case. • Present a novel mechanism combining network RIM problem and game theory. • It is the first work to use the Shapley value to model and solve the RIM problem. • Newly introduce one realistic/uncertain IC model with centrality measures. • The proposed algorithm outperforms latest methods under the worst diffusion case. • The novel parallel computing based framework is efficient for RIM solving. [ABSTRACT FROM AUTHOR]

We consider an inertial proximal strictly contractive Peaceman–Rachford splitting method (abbreviated as IPSCPRSM) with an indefinite proximal term for solving convex optimization problems with linear constraints. With the aid of variational inequality, proximal point method and fundamental inequality, we prove global convergence of the proposed method and analyze iteration complexity in the best function value and feasibility residues. The experimental results demonstrate the efficiency of the inertial extrapolation step and the indefinite proximal term even compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose two manifold regularization (MR) based distributed semi-supervised learning (DSSL) algorithms using the random vector functional link (RVFL) network and alternating direction method of multipliers (ADMM) strategy. In DSSL problems, training data consisting of labeled and unlabeled samples are often large-scale or high-dimension and split across samples or features. These distributed data separately stored over a communication network where each node has only access to its own data and can only communicate with its neighboring nodes. In many scenarios, centralized algorithms cannot be applied to solve DSSL problems. In our previous work, we proposed a MR based DSSL algorithm, denoted as the D-LapWNN algorithm, to solve DSSL problems with distributed samples. It has been proved that the D-LapWNN algorithm, combining the wavelet neural network (WNN) with the zero-gradient-sum (ZGS) strategy, is an efficient DSSL algorithm with distributed samples or horizontally partitioned data. The drawback of the D-LapWNN algorithm is that the loss function of each node or agent over the communication network must be twice continuously differentiable. In order to extend our previous work and settle the corresponding drawback, we propose a horizontally DSSL (HDSSL) algorithm to solve DSSL problems with distributed samples. Then, we novelly propose a vertically DSSL (VDSSL) algorithm to solve DSSL problems with distributed features or vertically partitioned data. As far as we know, the VDSSL algorithm is the first work focusing on DSSL problems with distributed features. During the learning process of the proposed algorithms, nodes over the communication network only exchange coefficients rather than raw data. It means that the proposed algorithms are privacy-preserving methods. Finally, some simulations are given to show the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

• Present a novel mechanism combining network reciprocity and indirect reciprocity. • The effects of initial reputation distribution and parameters are analyzed. • An interesting strategy updating rule based on reputation and payoff are introduced. • We find that large scale of reputation accumulation promotes cooperation. • Our conclusions universe for various network topologies. Indirect reciprocity is a fascinating topic in the field of social cooperation. In this paper, we propose a novel updating strategy based on the critical reputation-aware calculation. The joint of reputation allows players to make decisions not only on current payoffs but also from a third party, which improves the status of cooperators in the prisoner's dilemma game and provides a possibility for surviving. Experiments show that the discrepancies in initial fitness caused by reputation will support cooperators in occupying a high proportion in communities. Interestingly, we find that the massive scale of reputation fluctuation helps to enhance the cooperative effect, and newly name this character as "quasi-time lag". The simulations show that the promotion of our proposed mechanism is effective and robust on different network topologies. This work provides a new perspective for the study of the cooperative game. [ABSTRACT FROM AUTHOR]

Malaria is the world's most prevalent mosquito-borne disease caused by Plasmodium parasites, and responsible for over half a million deaths per year. To understand the effects of the intrinsic and extrinsic incubation periods of the parasite within the humans and mosquitoes, respectively, and the seasonality on disease transmission, we propose a periodic malaria model with delays. The basic reproduction number R 0 is derived, and it is shown that R 0 is a threshold parameter between the extinction and persistence of the disease. In the case where all the coefficients are constants and the intrinsic incubation period is ignored, we also prove the global attractivity of the endemic equilibrium when R 0 > 1. Numerical simulations indicate that prolonging the incubation period in mosquitoes is more effective than prolonging the incubation period in humans for disease control. It is also found that increasing the strength of seasonal forcing will lead to a higher epidemic peak. • A periodic malaria model with two delays is proposed, one delay describing the EIP and the other representing the IIP. • The definition and numerical estimation of the basic reproduction number R 0 are given. • The threshold dynamics of the model in terms of R 0 is studied. • Exploring the influences of seasonality, the EIP and IIP on the disease transmission. [ABSTRACT FROM AUTHOR]

The strictly contractive Peaceman–Rachford splitting method (SCPRSM) has received a tremendous amount of attention for solving linearly constrained separable convex optimization problems. In this paper, we propose an indefinite proximal SCPRSM with substitution procedure (abbreviated as PPRSM-S) to improve numerical results. The prediction step takes a proximal SCPRSM cycle to update the variable blocks, then the correction step corrects the output slightly by computing a combination of the prediction step and the previous iteration. We derive the global convergence of the proposed method and analyze the convergence rate results under much mild conditions. Some experimental results on LASSO and total variation-based denoising problems demonstrate the efficiency of the substitution step and the indefinite proximal term. [ABSTRACT FROM AUTHOR]

In this note we present a counterexample to Seeger's conjecture on the convexity of sums of singular values of rectangular matrices. Thus we answer the related open question in the negative. [ABSTRACT FROM AUTHOR]

At present, the identification of amyloid becomes more and more essential and meaningful. Because its mis-aggregation may cause some diseases such as Alzheimer's and Parkinson's diseases. This paper focus on the classification of amyloidogenic peptides and a novel feature representation called PhyAve_PSSMDwt is proposed. It includes two parts. One is based on physicochemical properties involving hydrophilicity, hydrophobicity, aggregation tendency, packing density and H-bonding which extracts 15-dimensional features in total. And the other is 60-dimensional features through recursive feature elimination from PSSM by discrete wavelet transform. In this period, sliding window is introduced to reconstruct PSSM so that the evolutionary information of short sequences can still be extracted. At last, the support vector machine is adopted as a classifier. The experimental result on Pep424 dataset shows that PSSM's information makes a great contribution on performance. And compared with other existing methods, our results after cross-validation increase by 3.1%, 3.3%, 0.136 and 0.007 in accuracy, specificity, Matthew's correlation coefficient and AUC value, respectively. It indicates that our method is effective and competitive. • A feature representation called PhyAve_PSSMDwt is applied to identify amyloidogenic peptides. • We introduce sliding windows so that the evolutionary information on PSSM can still be extracted from short sequence. • PhyAve_PSSMDwt model outperforms some other existing methods. [ABSTRACT FROM AUTHOR]

We describe a new two-step iterative method for solving the absolute value equations Ax−|x|=b, which is an NP-hard problem. This method is globally convergent under suitable assumptions. Numerical examples are given to demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

To globally solve a nonconvex quadratic programming problem, this paper presents an accelerating linearizing algorithm based on the framework of the branch-and-bound method. By utilizing a new linear relaxation approach, the initial quadratic programming problem is reduced to a sequence of linear relaxation programming problems, which is used to obtain a lower bound of the optimal value of this problem. Then, by using the deleting operation of the investigated regions, we can improve the convergent speed of the proposed algorithm. The proposed algorithm is proved to be convergent, and some experiments are reported to show higher feasibility and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with description of the existence and the forms of entire solutions of several second-order partial differential-difference equations with more general forms of Fermat type. By utilizing the Nevanlinna theory of meromorphic functions in several complex variables we obtain some results on the forms of entire solutions for these equations, which are some extensions and generalizations of the previous theorems given by Xu and Cao (Mediterr. J. Math. 15:1–14, 2018; Mediterr. J. Math. 17:1–4, 2020) and Liu et al. (J. Math. Anal. Appl. 359:384–393, 2009; Electron. J. Differ. Equ. 2013:59–110, 2013; Arch. Math. 99:147–155, 2012). Moreover, by some examples we show the existence of transcendental entire solutions with finite order of such equations. [ABSTRACT FROM AUTHOR]

In this paper, we studied a class of nonlocal nonlinear Schrödinger systems modeling the propagation of two polarized and coherent light beams in nematic liquid crystals. The existences of local and global solutions for the corresponding Cauchy problems were derived first upon applying Strichartz's estimates and conservation laws. The solitary wave solutions of the Schrödinger system were investigated next, and the existence of positive radially ground state vector solitary wave solutions was proved by using the rearrangement method and the minimization argument. [ABSTRACT FROM AUTHOR]

The purpose of this article is to study the uniqueness of meromorphic functions on annuli. Under a certain condition about deficiencies, we prove some new uniqueness theorems of meromorphic functions on the annulus A = z : 1 / R 0 < z < R 0 , where 1 < R 0 ≤ + ∞. [ABSTRACT FROM AUTHOR]

• A novel two-stage optimization method is proposed for network community partition. • Clever mechanism with the centrality information for referencing new benchmarks. • New affinity matrix is built to solve cases missing nodewise similarities. • Variational primal-dual analysis for the convex optimization problem. • Our optimization method stays high performance under shortage of benchmark nodes. In this paper, we proposed a novel two-stage optimization method for network community partition, which is based on inherent network structure information. The introduced optimization approach utilizes the new centrality measure of both network links and vertices to construct the key affinity description of the given network, while the direct similarities between graph nodes or nodal features are not available to obtain the classical affinity matrix. Indeed, such calculated network centrality information not only encodes the essential hints for detecting meaningful network communities, but also introduces a 'confidence' criterion for referencing new labeled benchmark nodes. For the resulted challenging combinatorial optimization problem of graph clustering, the proposed optimization method iteratively employs an efficient convex optimization algorithm which is developed based under a new variational perspective of primal and dual. Experiments over both artificial and real-world network datasets demonstrate that the proposed optimization strategy of community partition significantly improves results and outperforms the state-of-the-art algorithms in terms of accuracy and robustness, while using much less annotated benchmark information. [ABSTRACT FROM AUTHOR]

Abstract System level diagnosis is an important approach for the fault diagnosis of multiprocessor systems. In system level diagnosis, diagnosability is an important measure of the diagnosis capability of interconnection networks. But as a measure, diagnosability can not reflect the diagnosis capability of multiprocessor systems to link faults which may occur in real circumstances. In this paper, we propose the definition of h -edge tolerable diagnosability to better measure the diagnosis capability of interconnection networks under hybrid fault circumstances. The h -edge tolerable diagnosability of a multiprocessor system G is the maximum number of faulty nodes that the system can guarantee to locate when the number of faulty edges does not exceed h , denoted by t h e (G). The PMC model and MM model are the two most widely studied diagnosis models for the system level diagnosis of multiprocessor systems. The hypercubes are the most well-known interconnection networks. In this paper, the h -edge tolerable diagnosability of n -dimensional hypercube under the PMC and MM ⁎ model is determined as follows: t h e (Q n) = n − h , where 1 ≤ h < n , n ≥ 4. [ABSTRACT FROM AUTHOR]

The genomic basis underlying the selection for environmental adaptation and yield-related traits in maize remains poorly understood. Here we carried out genome-wide profiling of the small RNA (sRNA) transcriptome (sRNAome) and transcriptome landscapes of a global maize diversity panel under dry and wet conditions and uncover dozens of environment-specific regulatory hotspots. Transgenic and molecular studies of Drought-Related Environment-specific Super eQTL Hotspot on chromosome 8 (DRESH8) and ZmMYBR38, a target of DRESH8-derived small interfering RNAs, revealed a transposable element-mediated inverted repeats (TE-IR)-derived sRNA- and gene-regulatory network that balances plant drought tolerance with yield-related traits. A genome-wide scan revealed that TE-IRs associate with drought response and yield-related traits that were positively selected and expanded during maize domestication. These results indicate that TE-IR-mediated posttranscriptional regulation is a key molecular mechanism underlying the tradeoff between crop environmental adaptation and yield-related traits, providing potential genomic targets for the breeding of crops with greater stress tolerance but uncompromised yield. Transposon-associated inverted repeats play a role in drought resistance and yield-related traits in maize. [ABSTRACT FROM AUTHOR]

• This article first attempts to solve the distributed adaptive consensus problem of a class of stochastic mixed first- and second-order periodic time-varying nonlinear parameterized MASs by using the NNs, FSE and adaptive design method. • The distributed control protocol designed in this article has a wide range of applications, because the modeling system of follower not only contains the dynamic of periodic time-varying nonlinear parameterization, but also considers the influence of random factors. • A new fully distributed control based on local neighbor information is designed by adding a time-varying adaptive gain and constructing a new Lyapunov functional. In this article, a new fully distributed neural control is presented for periodically time-varying parameterized stochastic nonlinear multi-agent systems with hybrid-order dynamics. All follower systems are not required to be nonlinear dynamics of the same-order. The unknown periodically time-varying nonlinear function is described by using neural networks and Fourier series expansion in the design. It is proved that the presented distributed adaptive method can ensure that all closed-loop signals are bounded in the sense of probability. Furthermore, the state variable of the closed-loop system can be proved to converge to an arbitrary small neighborhood of the zero in the mean square sense. Simulation results are presented to demonstrate the effectiveness of the proposed distributed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider the nonlinear second-order cone programming problem. By combining an SQP method and filter technique, we present a trust region SQP-filter method for solving this problem. The proposed algorithm avoids using the classical merit function with penalty term. Furthermore, under standard assumptions, we prove that the iterative sequence generated by the presented algorithm converges globally. Preliminary numerical results indicate that the algorithm is promising. [Copyright &y& Elsevier]

This paper is concerned with the existence and uniqueness of positive solution for tensor equations. Under suitable assumptions, we show that the concerned tensor equations have a unique positive solution. In particular, we show that our main result includes the existing result in literature as its special case. Finally, we illustrate one example to verify the obtained results. [ABSTRACT FROM AUTHOR]

• A novel adaptive law updated by the event-triggered sampled data is developed over the plant-control communication network. • The measurement error induced by the event-triggered mechanism is compensated by introducing a novel kind of dynamics. • The parameter mismatch difficulties for the event-triggered adaptive law design are tackled. In this paper, the adaptive event-triggered output regulation problem for the parametric strict feedback system is addressed. The adaptive control strategy over the plant-control communication network is developed and governed by the transmission sampled data of the plant, completely. By introducing an augment dynamic to compensate the event-triggered measurement errors, the event-triggered mechanism is developed independent on the stability of the adaptive algorithm. Therefore, a more flexible and robust event-triggered mechanism is proposed than the existing method where the threshold coefficients are constrained with the closed-loop system stable requirement. It is proved that, the asymptotical output regulation error upper bound is continuously dependent on the event-triggered threshold coefficient, but independent on the upper bound of unknown parameters. Therefore, the output regulation error upper bound can be modified by the feedback gains and threshold coefficients without considering the bounded unknown parameters. What's more, the Zeno phenomenon is discussed carefully and excluded. Finally, the event-triggered adaptive control method is applied to control the double-rod electro-hydraulic servo system. It is shown that, the proposed scheme has smaller output regulation error than the existing methods with less communication times. [ABSTRACT FROM AUTHOR]

The proximal term plays a significant role in the literature of proximal Alternating Direction Method of Multipliers (ADMM), since (positive-definite or indefinite) proximal terms can promote convergence of ADMM and further simplify the involved subproblems. However, an overlarge proximal parameter decelerates the convergence numerically, though the convergence can be established with it. In this paper, we thus focus on a Linearized Symmetric ADMM (LSADMM) with proper proximal terms for solving a family of multi-block separable convex minimization models, and we determine an optimal (smallest) value of the proximal parameter while convergence of this LSADMM can be still ensured. Our LSADMM partitions the data into two group variables and updates the Lagrange multiplier twice in different forms with suitable step sizes. The region of the proximal parameter, involved in the second group subproblems, is partitioned into the union of three different sets. We show the global convergence and sublinear ergodic convergence rate of LSADMM for the two cases, while a counter-example is given to illustrate that convergence of LSADMM can not be guaranteed for the remaining case. Theoretically, we obtain the optimal lower bound of the proximal parameter. Numerical experiments on the so-called latent variable Gaussian graphical model selection problems are presented to demonstrate performance of the proposed algorithm and the significant advantage of the optimal lower bound of the proximal parameter. [ABSTRACT FROM AUTHOR]

Sequencing technologies have been rapidly developed recently, leading to the breakthrough of sequencing-based clinical diagnosis, but accurate and complete genome variation benchmark would be required for further assessment of precision medicine applications. Despite the human cell line of NA12878 has been successfully developed to be a variation benchmark, population-specific variation benchmark is still lacking. Here, we established an Asian human variation benchmark by constructing and sequencing a stabilized cell line of a Chinese Han volunteer. By using seven different sequencing strategies, we obtained ~3.88 Tb clean data from different laboratories, hoping to reach the point of high sequencing depth and accurate variation detection. Through the combination of variations identified from different sequencing strategies and different analysis pipelines, we identified 3.35 million SNVs and 348.65 thousand indels, which were well supported by our sequencing data and passed our strict quality control, thus should be high confidence variation benchmark. Besides, we also detected 5,913 high-quality SNVs which had 969 sites were novel and located in the high homologous regions supported by long-range information in both the co-barcoding single tube Long Fragment Read (stLFR) data and PacBio HiFi CCS data. Furthermore, by using the long reads data (stLFR and HiFi CCS), we were able to phase more than 99% heterozygous SNVs, which helps to improve the benchmark to be haplotype level. Our study provided comprehensive sequencing data as well as the integrated variation benchmark of an Asian derived cell line, which would be valuable for future sequencing-based clinical development. [ABSTRACT FROM AUTHOR]

Abstract This paper aims to propose a distributed cooperative learning (DCL) algorithm for Radial Basis Function Network (RBFN), where training samples are often extremely large-scale, high-dimensional and located on distributed nodes over undirected and connected networks. The algorithm is investigated for RBFN with different basic functions, such as Gaussian function, multi-quadric function, thin plate spline function and so on. The proposed DCL–RBFN algorithm is based on Zero-Gradient-Sum (ZGS) distributed optimization strategy and works in a fully distributed fashion, which has no requirement on central node and communication of raw data from each node during the training process. Moreover, in order to avoid the waste of unnecessary network resources, an event-triggered communication mechanism is applied to our algorithm. Each node exchanges learned information with its neighboring nodes using the ZGS strategy under event-triggered mechanism. The convergence of the proposed DCL–RBFN and event-triggered DCL–RBFN algorithms are presented by using the Lyapunov method. It is proved that the proposed algorithms have exponential convergence speed and require less communication data over networks. Finally, a series of numerical simulations are given to verify the advantages of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Introduction: There is a consensus hidden in the criminal legislation of many countries that the criminal responsibility capacity of juvenile offenders is not significantly different from that of their peers. The purpose of this paper was to test this hypothesis. The research objects of this paper were 187 juvenile offenders in J Province, China, who are under detention measures, and 2,449 students from junior high school, senior high school and university in S Province as comparison objects. We subjected the gathered materials to independent-samples t -tests and one-way analysis of variance (ANOVA)., Results: (1) The self-control ability (109.30, 123.59) and empathy ability (63.86, 72.45) of juvenile offenders were significantly different from those of ordinary minors, but the difference of dialectical thinking ability was not statistically significant; (2) Except for the influence of mother's education level and family income on dialectical thinking ability, the other variables had no statistical significance on the three kinds of ability. Therefore, it was suggested that the correction plan and means for juvenile offenders should focus on the improvement of self-control ability and empathy ability., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Su, Fu, Li, Xu, Zhou and Jian.)

The influencing factors of internet fraud, including demographics, psychology, experience and knowledge of susceptibility, have been widely studied. Research on the psychological mechanism of the victimisation process of internet fraud is relatively scarce but suggests a new research perspective. To summarise and unify the research in this field, this study systematically searched and analysed articles on the psychological decision-making mechanism of online fraud victims. We found that (a) previous researchers consistently believed that the heuristic processing mode was correlated with susceptibility to online fraud and that the systematic processing mode was helpful to detect and identify fraud. From the overall review results, we do not reject this conclusion, but the verification and intrinsic explanation of this relationship need to be further strengthened. (b) Under the heuristic-systematic model (HSM), with the exception of the trait of suspicion, there is no consensus on whether psychological factors (e.g., personality) influence the likelihood of online fraud through the mediating effect of the selection of the two systems. Objective knowledge and experience in specific fields have been found to be able to achieve this path. Information on the influential variables of equipment and habits is emerging, but how they affect network victimisation through the heuristic processing system needs to be further clarified. (c) The measurement of variables is conducted through simulation experiments. There may be a gap between the likelihood of internet fraud victimisation in the simulation experiment and in the real world. (d) The defence strategies under the HSM are intentional explorations, such as content-based cue recognition technology and simulated scene training., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Shang, Wang, Tian, Zhou, Ma and Liu.)

In this paper, the event-triggered adaptive fault tolerant tracking control for the strict-feedback nonlinear system with mismatched unknown parameters, external disturbances and actuator faults is addressed. A novel adaptive fault tolerant mechanism is presented to overcome the difficulty arising from the actuator faults and mismatched parameters, where the parameters estimators are generated from discrete state governed by the state dependent event-triggered mechanism. Furthermore, by introducing the auxiliary dynamic, it is shown that all the signals of the system are ultimately bounded and the asymptotical output tracking error is within a small residual set including origin which can be modified arbitrarily small by tuning the control coefficients. Based on the presented adaptive fault tolerant mechanism, the robust event-triggered mechanism and adaptive algorithm are further developed and the boundedness of all the variables of the system is shown. What is more, it is proved that both of the presented event-triggered mechanisms are Zeno-free. Finally, the control effectiveness of the proposed fault tolerant strategy is discussed and verified by a numerical example and the single-link manipulator., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

Finding a seed set to propagate more information within a specific budget is defined as the influence maximization (IM) problem. The traditional IM model contains two cardinal aspects: (i) the influence propagation model and (ii) effective/efficient seed-seeking algorithms. However, most of models only consider one kind of node (i.e., influential nodes), ignoring the role of other nodes (e.g., boosting nodes) in the spreading process, which are irrational. Specifically, in the real-world propagation scenario, the boosting nodes always improve the spread of influence from the initial activated seeds, which is an efficient and cost-economic measure. In this paper, we consider the realistic budgeted influence maximization (RBIM) problem, which contains two kind of nodes to improve the diffusion of influence. Facing the newly modified objective function, we propose a novel B-degree discount algorithm to solve it. The novel B-degree discount algorithm which adopts the cost-economic boosting nodes to retweet the influence from the predecessor nodes can greatly reduce the cost, and performs better than other state-of-the-art algorithms in both effect and efficiency on RBIM problem solving.

The importance of structural variants (SVs) for human phenotypes and diseases is now recognized. Although a variety of SV detection platforms and strategies that vary in sensitivity and specificity have been developed, few benchmarking procedures are available to confidently assess their performances in biological and clinical research. To facilitate the validation and application of these SV detection approaches, we established an Asian reference material by characterizing the genome of an Epstein-Barr virus (EBV)-immortalized B lymphocyte line along with identified benchmark regions and high-confidence SV calls. We established a high-confidence SV callset with 8938 SVs by integrating four alignment-based SV callers, including 109× Pacific Biosciences (PacBio) continuous long reads (CLRs), 22× PacBio circular consensus sequencing (CCS) reads, 104× Oxford Nanopore Technologies (ONT) long reads, and 114× Bionano optical mapping platform, and one de novo assembly-based SV caller using CCS reads. A total of 544 randomly selected SVs were validated by PCR amplification and Sanger sequencing, demonstrating the robustness of our SV calls. Combining trio-binning-based haplotype assemblies, we established an SV benchmark for identifying false negatives and false positives by constructing the continuous high-confidence regions (CHCRs), which covered 1.46 gigabase pairs (Gb) and 6882 SVs supported by at least one diploid haplotype assembly. Establishing high-confidence SV calls for a benchmark sample that has been characterized by multiple technologies provides a valuable resource for investigating SVs in human biology, disease, and clinical research., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Trachypithecus leucocephalus, the white-headed langur, is a critically endangered primate that is endemic to the karst mountains in the southern Guangxi province of China. Studying the genomic and transcriptomic mechanisms underlying its local adaptation could help explain its persistence within a highly specialized ecological niche., Results: In this study, we used PacBio sequencing and optical assembly and Hi-C analysis to create a high-quality de novo assembly of the T. leucocephalus genome. Annotation and functional enrichment revealed many genes involved in metabolism, transport, and homeostasis, and almost all of the positively selected genes were related to mineral ion binding. The transcriptomes of 12 tissues from three T. leucocephalus individuals showed that the great majority of genes involved in mineral absorption and calcium signaling were expressed, and their gene families were significantly expanded. For example, FTH1 primarily functions in iron storage and had 20 expanded copies., Conclusions: These results increase our understanding of the evolution of alkali tolerance and other traits necessary for the persistence of T. leucocephalus within an ecologically unique limestone karst environment.

Bread wheat expanded its habitat from a core area of the Fertile Crescent to global environments within ~10,000 years. The genetic mechanisms of this remarkable evolutionary success are not well understood. By whole-genome sequencing of populations from 25 subspecies within the genera Triticum and Aegilops, we identified composite introgression from wild populations contributing to a substantial portion (4-32%) of the bread wheat genome, which increased the genetic diversity of bread wheat and allowed its divergent adaptation. Meanwhile, convergent adaptation to human selection showed 2- to 16-fold enrichment relative to random expectation-a certain set of genes were repeatedly selected in Triticum species despite their drastic differences in ploidy levels and growing zones, indicating the important role of evolutionary constraints in shaping the adaptive landscape of bread wheat. These results showed the genetic necessities of wheat as a global crop and provided new perspectives on transferring adaptive success across species for crop improvement.

In this paper, we propose a distributed semi-supervised learning (DSSL) algorithm based on the extreme learning machine (ELM) algorithm over communication network using the event-triggered (ET) communication scheme. In DSSL problems, training data consisting of labeled and unlabeled samples are distributed over a communication network. Traditional semi-supervised learning (SSL) algorithms cannot be used to solve DSSL problems. The proposed algorithm, denoted as ET-DSS-ELM, is based on the semi-supervised ELM (SS-ELM) algorithm, the zero gradient sum (ZGS) distributed optimization strategy and the ET communication scheme. Correspondingly, the SS-ELM algorithm is used to calculate the local initial value, the ZGS strategy is used to calculate the globally optimal value and the ET scheme is used to reduce communication times during the learning process. According to the ET scheme, each node over the communication network broadcasts its updated information only when the event occurs. Therefore, the proposed ET-DSS-ELM algorithm not only takes the advantages of traditional DSSL algorithms, but also saves network resources by reducing communication times. The convergence of the proposed ET-DSS-ELM algorithm is guaranteed by using the Lyapunov method. Finally, some simulations are given to show the efficiency of the proposed algorithm., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

This paper aims to propose a distributed semi-supervised learning (D-SSL) algorithm to solve D-SSL problems, where training samples are often extremely large-scale and located on distributed nodes over communication networks. Training data of each node consists of labeled and unlabeled samples whose output values or labels are unknown. These nodes communicate in a distributed way, where each node has only access to its own data and can only exchange local information with its neighboring nodes. In some scenarios, these distributed data cannot be processed centrally. As a result, D-SSL problems cannot be centrally solved by using traditional semi-supervised learning (SSL) algorithms. The state-of-the-art D-SSL algorithm, denoted as Distributed Laplacian Regularization Least Square (D-LapRLS), is a kernel based algorithm. It is essential for the D-LapRLS algorithm to estimate the global Euclidian Distance Matrix (EDM) with respect to total samples, which is time-consuming especially when the scale of training data is large. In order to solve D-SSL problems and overcome the common drawback of kernel based D-SSL algorithms, we propose a novel Manifold Regularization (MR) based D-SSL algorithm using Wavelet Neural Network (WNN) and Zero-Gradient-Sum (ZGS) distributed optimization strategy. Accordingly, each node is assigned an individual WNN with the same basis functions. In order to initialize the proposed D-SSL algorithm, we propose a centralized MR based SSL algorithm using WNN. We denote the proposed SSL and D-SSL algorithms as Laplacian WNN (LapWNN) and distributed LapWNN (D-LapWNN), respectively. The D-LapWNN algorithm works in a fully distributed fashion by using ZGS strategy, whose convergence is guaranteed by the Lyapunov method. During the learning process, each node only exchanges local coefficients with its neighbors rather than raw data. It means that the D-LapWNN algorithm is a privacy preserving method. At last, several illustrative simulations are presented to show the efficiency and advantage of the proposed algorithm., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Wheat peroxidases 1 (WP1) is the major cationic peroxidase of wheat (Triticum aestivum) grain, which is involved in the development of seeds and an important factor to affect the final processing quality of flour. We constructed a prokaryotic expression vector pET28a-WP1, and transformed it into E. coli host strain T7 Expression. His-tag fused WP1 existed as inclusion body, and the recombinant protein was purified by Ni-NTA resin affinity chromatography under denatured condition. The purity of target protein reached 98%. The recombinant WP1 was refolded by gradient urea dialysis, then used as antigen to immune rabbit to prepare polyclonal antibody. The result of ELISA showed that the titer of rabbit anti-WP1 antiserum was higher than 1:625 000. The result of Western blotting demonstrated that the prepared WP1 polyclonal antibody could be used to detect WP1 with high specificity.