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In this paper, we focus on two tasks: semantic segmentation and target detection in the visual semantic understanding of tennis sports images and we optimize the network structure to achieve a more complete location contour information mining of the target. In detail, we focus on a weakly supervised image semantic segmentation method based on null convolution pixel relations. To address the problem of incomplete pixel-level pseudo-labeling, we introduce a cavity convolution unit with multiple cavity rates and a self-attentive mechanism in the classification model to adaptively enhance the target regions and suppress other irrelevant regions while expanding the perceptual field to generate high-quality pixel-level pseudo-labeling and then train the semantic segmentation model. The final experimental results show that the hierarchical fusion algorithm proposed in this paper significantly outperforms other algorithms, and the overall classification accuracy of the tandem cavity neural network algorithm reaches 81% with good overall classification results. The recognition accuracy of static movements is higher than that of dynamic movements. [ABSTRACT FROM AUTHOR]

This note provides an error estimate for finite element approximation to elliptic partial differential equations (PDEs) with discontinuous Dirichlet boundary data. Solutions of problems of this type are not in H 1 and, hence, the standard variational formulation is not valid. To circumvent this difficulty, an error estimate of a finite element approximation in the W 1 , r (Ω) (0 < r < 2) norm is obtained through a regularization by constructing a continuous approximation of the Dirichlet boundary data. [ABSTRACT FROM AUTHOR]

A novel calix[6] arene polymer stationary phase (C6A-PEG) is reported that combines the π-electron-rich cavity of calix[6] arene (C6A) with polyethylene glycol (PEG) units for gas chromatography (GC). Thermogravimetric analysis revealed a 5% weight loss of C6A-PEG at 318 °C, and its static-coated capillary column showed high efficiency of 2924 plates/m (k = 3.48) determined by 1-octanol at 120 °C. As a result, the moderate polar C6A-PEG column showed clearly advantageous performance for the Grob reagent and a mixture of 25 analytes with high selectivity for hydrogen-bonded acids. Also, it exhibited high resolving performance for various isomer mixtures, in particular high-resolution performance (R > 2.2) for the challenging toluidine and dimethylaniline systems with distinct advantages compared to the C6A and PEG columns (R < 1.5 for some pairs). This work demonstrates the potential of calixarene-based polymers as novel stationary phases for gas chromatographic analysis. [ABSTRACT FROM AUTHOR]

In this paper, we present a class of discontinuous Galerkin finite element methods for advection-diffusion-reaction problems and establish a priori error estimates when the solution is only in H 1 + s (Ω) with s ∈ (0 , 1 / 2 ]. [ABSTRACT FROM AUTHOR]

In this work, a novel star‐shaped copolymer functionalized by benzimidazolium ionic liquid (Star‐PEG‐PCL2‐2Bim) was synthesized and applied as stationary phase in capillary gas chromatography (GC). After static coating, the Star‐PEG‐PCL2‐2Bim column exhibited a column efficiency of 2655 plates/m and moderate polarity. The Star‐PEG‐PCL2‐2Bim column showed preferential retention and high resolving capability for hydrogen‐bonded and aromatic analytes, showing advantages over the ordinary polysiloxane phase. Impressively, it attained high resolution for the isomer mixtures of halogenated benzene, nitrobenzene, phenol and benzaldehydes with good peak shapes. The advantageous separation performance shows the good potential of the Star‐PEG‐PCL2‐2Bim stationary phase for GC analysis. [ABSTRACT FROM AUTHOR]

• Proposing a self-adaptive algorithm (ANE) for designing a nearly optimal two-layer NN for solving PDEs. • A method of continuation by the ANE method for providing a good initialization in training the neural network. • Analyzing effect of numerical integration. • Introducing a posteriori error estimators of recovery type for the ANE method. • Demonstrating superior performance of the ANE method for problems with interface singularities and sharp interior layers. In the companion paper [1] , we introduce adaptive network enhancement (ANE) method for the best least-squares approximation to a target function by using two-layer ReLU neural networks (NNs). In this paper, we apply the ANE method for solving self-adjoint second-order elliptic partial differential equations (PDEs). The underlying PDE is discretized by the Ritz method using a two-layer spline neural network based on either the primal or dual formulations that minimize the respective energy or complimentary functionals. Essential boundary conditions are imposed weakly through the functionals with proper norms. It is proved that the Ritz approximation is the best approximation in the energy norm; moreover, effect of numerical integration for the Ritz approximation is analyzed as well. Two estimators for adaptive neuron enhancement method are introduced, one is the so-called recovery estimator and the other is the least-squares estimator. Finally, numerical results for diffusion problems with either corner or intersecting interface singularities are presented. [ABSTRACT FROM AUTHOR]

In this paper, we introduce adaptive network enhancement (ANE) method for the best least-squares approximation using two-layer ReLU neural networks (NNs). For a given function f (x) , the ANE method generates a two-layer ReLU NN and a numerical integration mesh such that the approximation accuracy is within the prescribed tolerance. The ANE method provides a natural process for obtaining a good initialization which is crucial for training nonlinear optimization problems. Numerical results for functions of two variables exhibiting either intersecting interface singularities or sharp interior layers demonstrate efficiency of the ANE method. [ABSTRACT FROM AUTHOR]

In [7] , we introduced the least-squares ReLU neural network (LSNN) method for solving the linear advection-reaction problem with discontinuous solution and showed that the method outperforms mesh-based numerical methods in terms of the number of degrees of freedom. This paper studies the LSNN method for scalar nonlinear hyperbolic conservation law. The method is a discretization of an equivalent least-squares (LS) formulation in the set of neural network functions with the ReLU activation function. Evaluation of the LS functional is done by using numerical integration and conservative finite volume scheme. Numerical results of some test problems show that the method is capable of approximating the discontinuous interface of the underlying problem automatically through the free breaking lines of the ReLU neural network. Moreover, the method does not exhibit the common Gibbs phenomena along the discontinuous interface. [ABSTRACT FROM AUTHOR]

• A reliability optimization model of CMSs under limited cost budgets is constructed. • A time series importance measure is proposed to evaluate key machines in CMSs. • TIACO combines the local search ability of TIM and the global search ability of ACO. Cellular manufacturing systems (CMSs) can improve the quality and efficiency of the manufacturing process by multiple processing cells with different functions and group technology. CMSs require high reliability to complete processing missions successively, so reliability optimization is an important part to guarantee system performance. This paper proposes a binary decision diagram-based three-step evaluation method to analyze CMS reliability. A reliability optimization model of CMS is constructed by considering the limited cost to determine the optimal combination of machine degradation parameters. Considering the advantages of ant colony optimization (ACO) and time series importance measure (TIM), a TIM-based ant colony optimization (TIACO) is developed to solve the optimization model. To verify the performance of TIACO, system reliability and running time are introduced to compare with genetic algorithm (GA), ACO, and time series importance measure-based genetic algorithm (TIGA). (1) System reliability obtained by TIACO is always the best. (2) Running time of TIACO is smaller. A case study of an unmanned aerial vehicle manufacturing company verifies the effectiveness of TIACO, and machines with higher TIMs should be given priority to improving their degradation parameters, which provides a new idea for reliability evaluation and optimization of CMSs. [ABSTRACT FROM AUTHOR]

A least-squares neural network (LSNN) method was introduced for solving scalar linear and nonlinear hyperbolic conservation laws (HCLs) in Cai et al. (2021, 2022). This method is based on an equivalent least-squares (LS) formulation and uses ReLU neural network as approximating functions, making it ideal for approximating discontinuous functions with unknown interface location. In the design of the LSNN method for HCLs, the numerical approximation of differential operators is a critical factor, and standard numerical or automatic differentiation along coordinate directions can often lead to a failed NN-based method. To overcome this challenge, this paper rewrites HCLs in their divergence form of space and time and introduces a new discrete divergence operator. As a result, the proposed LSNN method is free of penalization of artificial viscosity. Theoretically, the accuracy of the discrete divergence operator is estimated even for discontinuous solutions. Numerically, the LSNN method with the new discrete divergence operator was tested for several benchmark problems with both convex and non-convex fluxes, and was able to compute the correct physical solution for problems with rarefaction, shock or compound waves. The method is capable of capturing the shock of the underlying problem without oscillation or smearing, even without any penalization of the entropy condition, total variation, and/or artificial viscosity. • A new discrete divergence operator is designed to approximate discontinuous solutions. • The resulting LSNN method is free of penalizations of artificial viscosity, entropy, etc. • It sharply captures shock, rarefaction, compound waves without oscillation or smearing. [ABSTRACT FROM AUTHOR]

In Cai, He, and Zhang (2017), we studied an improved Zienkiewicz-Zhu (ZZ) a posteriori error estimator for conforming linear finite element approximation to diffusion problems. The estimator is more efficient than the original ZZ estimator for non-smooth problems, but with comparable computational costs. This paper extends the improved ZZ estimator for discontinuous linear finite element approximations including both nonconforming and discontinuous elements. In addition to post-processing a flux, we further explicitly recover a gradient in the H (curl) conforming finite element space. The resulting error estimator is proved, theoretically and numerically, to be efficient and reliable with constants independent of the jump of the coefficient regardless of its distribution. [ABSTRACT FROM AUTHOR]

• Interactions among quality, inspection performance and degradation are modeled. • Inspection's restraints in the propagation of unqualified products are analyzed. • Multiple degradations are modeled by Gamma, marked point and wiener stochastic process. • System reliability of SMSII is firstly proposed considering imperfect inspection. Reliability evaluation of manufacturing system always takes machine performance and product quality as two key factors. But the imperfect inspection usually leads to unreliable quality judgments, cause the propagation of unqualified products, and aggravate the degradation of downstream machines. In this paper, considering the effect of imperfect inspection process, a new reliability model is developed based on the interaction between machine performance and product quality. Firstly, multiple degradation modes, which include continuous and discrete degradations, are modeled by different stochastic processes. Secondly, a quality reliability model is established to evaluate the satisfaction rate of production demand based on the variation estimation model of product's dimensions. Thirdly, the four states (Q-Q, U-U, Q-U, and U-Q) of quality inspection process are defined, whose effects on discrete degradation are analyzed in qualitative by referring to the quality evaluation. Finally, a system reliability evaluation model is proposed based on the analysis of minimum target requirements, and the practical case of a connecting rod illustrates the effectiveness of the proposed model. The results indicate that the abundant information existing in manufacturing system can be effectively integrated and exploited by the proposed reliability model, and the weakness of manufacturing system can also be identified easily. [ABSTRACT FROM AUTHOR]

In this paper, we study the deep Ritz method for solving the linear elasticity equation from a numerical analysis perspective. A modified Ritz formulation using the H 1 / 2 (Γ D) norm is introduced and analyzed for linear elasticity equation in order to deal with the (essential) Dirichlet boundary condition. We show that the resulting deep Ritz method provides the best approximation among the set of deep neural network (DNN) functions with respect to the "energy" norm. Furthermore, we demonstrate that the total error of the deep Ritz simulation is bounded by the sum of the network approximation error and the numerical integration error, disregarding the algebraic error. To effectively control the numerical integration error, we propose an adaptive quadrature-based numerical integration technique with a residual-based local error indicator. This approach enables efficient approximation of the modified energy functional. Through numerical experiments involving smooth and singular problems, as well as problems with stress concentration, we validate the effectiveness and efficiency of the proposed deep Ritz method with adaptive quadrature. [ABSTRACT FROM AUTHOR]

• Dynamic of machine reliability, WIP quality, and production velocity are modeled. • Structural complexity and reliability-quality interactions in manufacturing system are studied. • Joint actions of maintenance and quality inspection are constructed in DRL model. • The DDPG algorithm enables effective learning with dynamic manufacturing networks. Most existing studies on joint optimization of manufacturing systems (MS) focus on small-scale systems with simple structures, such as the single-machine, simple serial, or parallel MS. Simultaneously, traditional algorithms utilized in small-scale MS always show an insufficiency in solving large-scale dynamic MS with complex structures, such as manufacturing networks. Therefore, considering the effectiveness of reinforcement learning on the infinite-horizon Markov Decision Process (MDP), this paper presents a joint optimization problem of preventive maintenance and work-in-process quality inspection for manufacturing networks with reliability-quality interactions. First, dynamic reliability and quality models are proposed at the machine level to cope with complex interactions in manufacturing networks. Second, based on the MDP-based optimization model, the proposed Deep Deterministic Policy Gradient (DDPG) algorithm realizes the optimal reliability-quality joint control in manufacturing networks. Besides, it also offers a novel mixed action space containing discrete maintenance and continuous quality inspection, which could satisfy the action diversity in actual production. At last, training and experiments imply our algorithm is more adaptable to diverse manufacturing scenarios than traditional ones. Also, it is proved that more-frequent state observations for learning cannot help the constructed reinforcement learning model get a better control policy because of the information redundancy. [ABSTRACT FROM AUTHOR]

Abstract An algorithm on computing accurate finite element approximation to the Poisson equation on a polygonal domain with corner singularities was studied in Kim and Lee (2016, 2017) numerically. The algorithm requires several iterations depending on singularities of the solution. Each iteration requires a solution of the standard finite element approximation to the Poisson equation with possible different Dirichlet data and the corresponding stress intensity factors. This paper provides an error estimate of the finite element approximation given by the algorithm, and, hence, determine the number of iterations needed to achieve full rates of convergence in both the energy and the L 2 norms. [ABSTRACT FROM AUTHOR]

In this study we developed and evaluated a crowdsourcing-based latent semantic analysis (LSA) approach to computerized summary scoring (CSS). LSA is a frequently used mathematical component in CSS, where LSA similarity represents the extent to which the to-be-graded target summary is similar to a model summary or a set of exemplar summaries. Researchers have proposed different formulations of the model summary in previous studies, such as pregraded summaries, expert-generated summaries, or source texts. The former two methods, however, require substantial human time, effort, and costs in order to either grade or generate summaries. Using source texts does not require human effort, but it also does not predict human summary scores well. With human summary scores as the gold standard, in this study we evaluated the crowdsourcing LSA method by comparing it with seven other LSA methods that used sets of summaries from different sources (either experts or crowdsourced) of differing quality, along with source texts. Results showed that crowdsourcing LSA predicted human summary scores as well as expert-good and crowdsourcing-good summaries, and better than the other methods. A series of analyses with different numbers of crowdsourcing summaries demonstrated that the number (from 10 to 100) did not significantly affect performance. These findings imply that crowdsourcing LSA is a promising approach to CSS, because it saves human effort in generating the model summary while still yielding comparable performance. This approach to small-scale CSS provides a practical solution for instructors in courses, and also advances research on automated assessments in which student responses are expected to semantically converge on subject matter content. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid a posteriori error estimator for the conforming finite element method, which may be regarded as a combination of the explicit residual and the improved ZZ error estimators. With comparable cost, the hybrid estimator is more accurate than the residual estimator. It is shown that the hybrid estimator is reliable on all meshes, unlike estimators of the ZZ type. Moreover, the reliability constant is independent of the jump of the diffusion coefficients for elliptic interface problems under the monotonicity assumption of the coefficients. Finally, numerical examples confirm the robustness of the estimator with respect to coefficient jumps and also better effectivity index compared to the residual estimator. [ABSTRACT FROM AUTHOR]

In recent years, with the development of societies and economies, the demand for social electricity has further increased. The efficiency and accuracy of electric-load forecasting is an important guarantee for the safety and reliability of power system operation. With the sparrow search algorithm (SSA), long short-term memory (LSTM), and random forest (RF), this research proposes an SSA-LSTM-RF daily peak-valley forecasting model. First, this research uses the Pearson correlation coefficient and the random forest model to select features. Second, the forecasting model takes the target value, climate characteristics, time series characteristics, and historical trend characteristics as input to the LSTM network to obtain the daily-load peak and valley values. Third, the super parameters of the LSTM network are optimized by the SSA algorithm and the global optimal solution is obtained. Finally, the forecasted peak and valley values are input into the random forest as features to obtain the output of the peak-valley time. The forest value of the SSA-LSTM-RF model is good, and the fitting ability is also good. Through experimental comparison, it can be seen that the electric-load forecasting algorithm based on the SSA-LSTM-RF model has higher forecasting accuracy and provides ideal performance for electric-load forecasting with different time steps. [ABSTRACT FROM AUTHOR]

The continuous state system is a special kind of a system in which the states of the system and its components have continuous values, ranging from perfect functioning to complete failure. This paper introduces the performance improvement for a continuous state system, which can be used to measure the improvement of systems performance comparing pre- and postmaintenance time. The probabilistic characteristics of performance improvement are discussed in detail. Then, the performance improvement for multicomponent maintenance and corresponding calculation method are also put forward to establish the objective function for maintenance optimization. Third, a maintenance optimization model for such a system is studied, and corresponding performance improvement based genetic algorithm is provided to search a near global optimal solution. Finally, two numerical examples and an oil transportation system application case study are implemented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article describes conversation-based assessments with computer agents that interact with humans through chat, talking heads, or embodied animated avatars. Some of these agents perform actions, interact with multimedia, hold conversations with humans in natural language, and adaptively respond to a person’s actions, verbal contributions, and emotions. Data are logged throughout the interactions in order to assess the individual’s mastery of subject matters, skills, and proficiencies on both cognitive and noncognitive characteristics. There are different agent-based designs that focus on learning and assessment. Dialogues occur between one agent and one human, as in the case of intelligent tutoring systems. Three-party conversations, called trialogues, involve two agents interacting with a human. The two agents can take on different roles (such as tutors and peers), model actions and social interactions, stage arguments, solicit help from the human, and collaboratively solve problems. Examples of assessment with these agent-based environments are presented in the context of intelligent tutoring, educational games, and interventions to help struggling adult readers. Most of these involve assessment at varying grain sizes to guide the intelligent interaction, but conversation-based assessment with agents is also currently being used in high stakes assessments. [ABSTRACT FROM AUTHOR]

In Cai and Zhang (2009), we introduced and analyzed an improved Zienkiewicz–Zhu (ZZ) estimator for the conforming linear finite element approximation to elliptic interface problems. The estimator is based on the piecewise “constant” flux recovery in the H ( div ; Ω ) conforming finite element space. This paper extends the results of Cai and Zhang (2009) to diffusion problems with full diffusion tensor and to the flux recovery both in piecewise constant and piecewise linear H ( div ) space. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel a posteriori error estimator for the conforming finite element approximation to the H ( curl ) problem with inhomogeneous media and with the right-hand side only in L 2 . The estimator is of the recovery type. Independent with the current approximation to the primary variable (the electric field), an auxiliary variable (the magnetizing field) is recovered in parallel by solving a similar H ( curl ) problem. An alternate way of recovery is presented as well by localizing of the error flux. The estimator is then defined as the sum of the modified element residual and the residual of the constitutive equation defining the auxiliary variable. It is proved that the estimator is approximately equal to the true error in the energy norm without the quasi-monotonicity assumption. Finally, we present numerical results for several H ( curl ) interface problems. [ABSTRACT FROM AUTHOR]

The optimization of linear consecutive- k -out-of- n (Lin/Con/ k / n ) is to find an optimal component arrangement where n components are assigned to n positions to maximize the system reliability. With the interchangeability of components in practical systems, the optimization of Lin/Con/ k / n systems is becoming widely applied in engineering practice, which is also a typical component assignment problem concerned by many researchers. This paper proposes a Birnbaum importance-based genetic algorithm (BIGA) to search the near global optimal solution for Lin/Con/ k / n systems. First, the operation procedures and corresponding execution methods of BIGA are described in detail. Then, comprehensive simulation experiments are implemented on both small and large systems to evaluate the performance of the BIGA by comparing with the Birnbaum importance-based two-stage approach and Birnbaum importance-based genetic local search algorithm. Thirdly, further experiments are provided to discuss the applicability of BIGA for Lin/Con/ k / n system with different k and n . Finally, the case study on oil transportation system is implemented to demonstrate the application of BIGA in the optimization of Lin/Con/ k / n system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Amphiphilic triblock copolymer (TCP-Bim) as GC stationary phase. • High resolving performance towards a wide range of analytes and isomers. • Baseline resolution among chloroaniline and bromoaniline isomers. • Studies on chromatographic retention mechanisms of target analytes on GC columns. In this work, a novel amphiphilic triblock copolymer bonded with benzimidazolium ionic liquid (TCP-Bim) was designed, synthesized, and employed as stationary phase for capillary gas chromatography (GC) column. This innovative column exhibited moderate polarity and an efficiency of 3280 plates/m, showing high resolutions for a wide range of analytes, with marked capabilities towards isomer separations, and different retention behaviors and separation performances respect to TCP or commercial HP-35 and PEG-20 M columns. The TCP-Bim stationary phase has exhibited in some cases unmatched separation capabilities, like, for example, in the case of separation of chloroaniline and bromoaniline. Its column performances have been compared with TCP and other commercial columns analyzing from apolar to polar compounds, showing in every case outstanding capability which clearly indicate that TCP-Bim can be considered a very interesting candidate as a new type of stationary phase in GC. [ABSTRACT FROM AUTHOR]

This article studies the least-squares finite element method for the linearized, stationary Navier-Stokes equation based on the stress-velocity-pressure formulation in d dimensions ( d = 2 or 3). The least-squares functional is simply defined as the sum of the squares of the L2 norm of the residuals. It is shown that the homogeneous least-squares functional is elliptic and continuous in the [ABSTRACT FROM AUTHOR]

Biodegradation of Paichongding (IPP), a recently developed cis -nitromethylene neonicotinoid insecticide, was investigated in two different soils under aerobic condition. IPP degradation rate was strongly affected by soil physic-chemical characteristics and the inoculation of IPP-degrading bacteria. Inoculation of IPP-degrading bacteria can increase degradation rate and decrease DT50 (half-life value). The removal ratio of RR-IPP, SS-IPP, SR-IPP and RS-IPP at 60 days after treatment (DAT) reached 30.17%, 28.06%, 51.48% and 45.76% in Yellow clayed soil (S1), 20.04%, 19.78%, 36.22% and 40.59% in Huangshi soil (S2), respectively. DT50 of IPP in S1 and S2 decreased after inoculation of Sphingobacterium sp. M3-1. Furthermore, based on the identified eight metabolites (M1–M8) by LC–MS/MS and their behavior, a biodegradation pathway of IPP in soils was proposed. New metabolites, M4, M6 and M7 were observed and determined in soils. Biodegradation of IPP involved continuous biocatalytic reactions such as nitro reduction and elimination, hydrolysis, C-N cleavage, de-methyl, and ether cleavage reactions. Finally, IPP was bio-transformed into M7 and M8. [ABSTRACT FROM AUTHOR]

This paper introduces a new recovery-based a posteriori error estimator for the lowest order Nédélec finite element approximation to the H ( curl ) interface problem. The error estimator is analyzed by establishing both the reliability and the efficiency bounds and is supported by numerical results. Under certain assumptions, it is proved that the reliability and efficiency constants are independent of the jumps of the coefficients. [ABSTRACT FROM AUTHOR]

A new asymptotically exact a posteriori error estimator is developed for first-order div least-squares (LS) finite element methods. Let ( u h , σ h ) be LS approximate solution for ( u , σ = − A ∇ u ) . Then, E = ‖ A − 1 / 2 ( σ h + A ∇ u h ) ‖ 0 is asymptotically exact a posteriori error estimator for ‖ A 1 / 2 ∇ ( u − u h ) ‖ 0 or ‖ A − 1 / 2 ( σ − σ h ) ‖ 0 depending on the order of approximate spaces for σ and u . For E to be asymptotically exact for ‖ A 1 / 2 ∇ ( u − u h ) ‖ 0 , we require higher order approximation property for σ , and vice versa. When both A ∇ u and σ are approximated in the same order of accuracy, the estimator becomes an equivalent error estimator for both errors. The underlying mesh is only required to be shape regular, i.e., it does not require quasi-uniform mesh nor any special structure for the underlying meshes. Confirming numerical results are provided and the performance of the estimator is explored for other choice of spaces for ( u h , σ h ) . [ABSTRACT FROM AUTHOR]

Myristyl serinate was firstly enzyme-synthesized from myristyl alcohol and l -serine in selected solvents (single and two-phase solvents). The effects of reaction system, two-phase system composition, temperature, pH, and reaction time on myristyl serinate synthesis were investigated. The results showed that two-phase solvent reaction system could significantly increase the enzymatic conversion ratio and the optimal ratio of two-phase solvent was 80:20 (dichloromethane:phosphate buffer). The optimal conditions for myristyl serinate enzymatic synthesis were substrate molar ratio of 1:1, Novozym 435 load of 5% (w/w), a solvent dosage of 20 ml, 30 °C and reaction time of 36 h. Under these conditions, 72.25% of myristyl alcohol could be converted to myristyl serinate, which was purified through column chromatography and identified by HPLC/IR/LC-MS/NMR. [ABSTRACT FROM AUTHOR]

• A self-adaptive algorithm (ANE) is proposed for designing an optimal DNN to approximate functions and PDEs. • ANE can automatically design a nearly minimal multi-layer NN to learn functions and PDEs with thin layers or discontinuities. • ANE provides a natural process for obtaining a good initialization in training. • Value of the least-squares functional is a good error estimator. Designing an optimal deep neural network for a given task is important and challenging in many machine learning applications. To address this issue, we introduce a self-adaptive algorithm: the adaptive network enhancement (ANE) method, written as loops of the form train → estimate → enhance. Starting with a small two-layer neural network (NN), the step train is to solve the optimization problem at the current NN; the step estimate is to compute a posteriori estimator/indicators using the solution at the current NN; the step enhance is to add new neurons to the current NN. Novel network enhancement strategies based on the computed estimator/indicators are developed in this paper to determine how many new neurons and when a new layer should be added to the current NN. The ANE method provides a natural process for obtaining a good initialization in training the current NN; in addition, we introduce an advanced procedure on how to initialize newly added neurons for a better approximation. We demonstrate that the ANE method can automatically design a nearly minimal NN for learning functions exhibiting sharp transitional layers as well as discontinuous solutions of hyperbolic partial differential equations. [ABSTRACT FROM AUTHOR]

Background: Microvascular invasion (MVI) has been reported to be an independent prognostic factor of recurrence and poor overall survival in patients with intrahepatic cholangiocarcinoma (ICC). This study aimed to explore the preoperative independent risk factors of MVI and establish a Bayesian network (BN) prediction model to provide a reference for surgical diagnosis and treatment. Methods: A total of 531 patients with ICC who underwent radical resection between 2010 and 2018 were used to establish and validate a BN model for MVI. The BN model was established based on the preoperative independent variables. The ROC curves and confusion matrix were used to assess the performance of the model. Results: MVI was an independent risk factor for relapse-free survival (RFS) (P < 0.05). MVI has a correlation with postoperative recurrence, early recurrence (< 6 months), median RFS and median overall survival (all P < 0.05). The preoperative independent risk variables of MVI included obstructive jaundice, prognostic nutritional index, CA19-9, tumor size, and major vascular invasion, which were used to establish the BN model. The AUC of the BN model was 78.92% and 83.01%, and the accuracy was 70.85% and 77.06% in the training set and testing set, respectively. Conclusion: The BN model established based on five independent risk variables for MVI is an effective and practical model for predicting MVI in patients with ICC. [ABSTRACT FROM AUTHOR]

Abstract: Magnesium alloy was disposed by three kinds of acid pickling formula and activation formula and the effect of the three kinds of acid activation formula on magnesium alloy was studied by contrast experiment. The experimental results indicated that after disposed by acid pickling formula of HNO3 25ml/L, H3PO4 25ml/L, room temperature and activation formula of NH4H2PO4 80–100g/L, NH4F 30–50g/L, room temperature, magnesium alloy could realize electroless nickel plating directly. The properties of the nickel plating layer and activation layer were researched by electrochemical polarization curves, X-ray diffraction and scanning electronic microscope and its energy spectrometer. The results showed that the structure of Ni–P coating was amorphous, the Ni–P coating was very meticulous and uniform, the activation coating was mainly MgF2 and Mg2P2O7, and comparing with magnesium substrate, the corrosion potential of magnesium alloys increased by about 1.1V and the corrosion current density declined obviously. Tested by thermal shock test and file test, the adhesion of magnesium alloy and Ni–P coating was good. [Copyright &y& Elsevier]

We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary distribution. Based on the fixed-point formulation of quasi-stationary distribution, we minimize the KL-divergence of two Markovian path distributions induced by candidate distribution and true target distribution. To solve this challenging minimization problem by gradient descent, we apply a reinforcement learning technique by introducing the reward and value functions. We derive the corresponding policy gradient theorem and design an actor-critic algorithm to learn the optimal solution and the value function. The numerical examples of finite state Markov chain are tested to demonstrate the new method. [ABSTRACT FROM AUTHOR]

The well-known Prager–Synge identity is valid in H 1 (Ω) and serves as a foundation for developing equilibrated a posteriori error estimators for continuous elements. In this paper, we introduce a new identity, that may be regarded as a generalization of the Prager–Synge identity, to be valid for piecewise H 1 (Ω) functions for diffusion problems. For nonconforming finite element approximation of arbitrary odd order, we improve the current methods by proposing a fully explicit approach that recovers an equilibrated flux in H (div ; Ø) through a local element-wise scheme. The local efficiency for the recovered flux is robust with respect to the diffusion coefficient jump regardless of its distribution. For discontinuous elements, we note that the typical approach of recovering a H 1 function for the nonconforming error can be proved robust only under some restrictive assumptions. To promote the unconditional robustness of the error estimator with respect to the diffusion coefficient jump, we propose to recover a gradient in H (curl ; Ω) space through a simple explicit averaging technique over facets. Our resulting error estimator is proved to be globally reliable and locally efficient regardless of the coefficient distribution. Nevertheless, the reliability constant is no longer to be 1. [ABSTRACT FROM AUTHOR]

ZJ0273, propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy) benzylamino) benzoate, is a novel and broad-spectrum herbicide. In this study, 15 bacteria capable of utilizing ZJ0273 as the sole carbon source were isolated from soil. One of the isolates belonged to the family Amycolatopsis and was designated to Amycolatopsis sp. M3-1; at 30°C and pH 7.0, degradation rate of ZJ0273 could reach at 59.3% and 68.5% in 25 days and 60 days, respectively. Furthermore, six metabolites (M1-M6) during the degradation of ZJ0273 by Amycolatopsis sp. M3-1 were identified by a combination with multi-position C-labeled compounds (B-ZJ0273 and C-ZJ0273), chromatography, liquid scintillation spectrometer, and LC-MS, a novel pathway of ZJ0273 degradation by Amycolatopsis sp. M3-1 was proposed based on the identified metabolites and their biodegradation courses. ZJ0273 was initially hydrolyzed into M1 (4-(2-(4,6-dimethoxypyrimidin-2-yloxy) benzylamino) benzoic acid), then further oxidized into M3 (2-(4,6-dimethoxypyrimidin-2-yloxy) benzoic acid). M1 also could undergo a carbonylation into M2 (4-(2-(4,6-dimethoxypyrimidin-2-yloxy) benzamido) benzoic acid), and then its C-N and C-O bonds were cleaved to yield M3 (2-(4,6-dimethoxypyrimidin-2-yloxy) benzoic acid) and M4 (4,6-dimethoxypyrimidin-2-ol), respectively. Moreover, another two new metabolites, M5 (2-(4-hydroxy, 6-methoxypyrimidin-2-yloxy) benzoic acid) and M6 (2, 4-dihydroxy-pyrimidine) were found. M5 was formed through de-methyl of M3 and then hydrolyzed into M6. [ABSTRACT FROM AUTHOR]

Background: It is important to identify gallbladder polyps (GPs) with malignant potential and avoid unnecessary cholecystectomy by constructing prediction model. The aim of the study is to develop a Bayesian network (BN) prediction model for GPs with malignant potential in a long diameter of 8–15 mm based on preoperative ultrasound. Methods: The independent risk factors for GPs with malignant potential were screened by χ2 test and Logistic regression model. Prediction model was established and validated using data from 1296 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China. A BN model was established based on the independent risk variables. Results: Independent risk factors for GPs with malignant potential included age, number of polyps, polyp size (long diameter), polyp size (short diameter), and fundus. The BN prediction model identified relationships between polyp size (long diameter) and three other variables [polyp size (short diameter), fundus and number of polyps]. Each variable was assigned scores under different status and the probabilities of GPs with malignant potential were classified as [0–0.2), [0.2–0.5), [0.5–0.8) and [0.8–1] according to the total points of [− 337, − 234], [− 197, − 145], [− 123, − 108], and [− 62,500], respectively. The AUC was 77.38% and 75.13%, and the model accuracy was 75.58% and 80.47% for the BN model in the training set and testing set, respectively. Conclusion: A BN prediction model was accurate and practical for predicting GPs with malignant potential patients in a long diameter of 8–15 mm undergoing cholecystectomy based on preoperative ultrasound. [ABSTRACT FROM AUTHOR]

• Two-dimensional warranties for manufacturers are proposed by considering limited number of random working cycles. • Random maintenance policies for consumer are investigated to sustain the reliability of products through warranties. • The proposed warranties and the investigated random maintenance policies are compared and discussed. Rapid development and wide application of cyber-physical systems (CPS) have created the possibility of remotely monitoring product working cycles. This indicates that CPS can provide the technological support required for designing manufacturers' warranties integrated with working cycle and for modelling users' (or consumers') random maintenance policies sustaining the reliability of the product through warranty. In this study, by designing limited working cycles as a warranty limit, we have proposed manufacturer's two-dimensional warranty first and last to ensure the product's reliability during the warranty coverage period. Subsequently, we have incorporated working cycle into the traditional maintenance policies and modelled users' random age replacement first and last to sustain the reliability of the product through its warranty. By analysing parameter values, we have offered some classic models, i.e., the expected long-run cost rates that represent certain special problems. Finally, sensitivities of both the proposed warranties and the modelled random age replacements have been analysed through numerical experiments. It has been demonstrated that whichever takes place first or last influences if the warranty cost of the proposed two-dimensional warranty is higher than that of the traditional one-dimensional warranty. Users can get a longer post-warranty period when the warranty limit m , i.e., the limited random working cycles, is lower. [ABSTRACT FROM AUTHOR]

Abstract: To identify the product failure rate grade under diverse configuration and operation conditions, a new conditional Bayesian networks (CBN) model is brought forward. By indicating the conditional independence relationship between attribute variables given the target variable, this model could provide an effective approach to classify the grade of failure rate. Furthermore, on the basis of the CBN model, the procedure of building product failure rate grade classifier is elaborated with modeling and application. At last, a case study is carried out and the results show that, with comparison to other Bayesian networks classifiers and traditional decision tree C4.5, the CBN model not only increases the total classification accuracy, but also reduces the complexity of network structure. [ABSTRACT FROM AUTHOR]

A planar printed loop antenna with hepta operating bands, which covers the GSM850/900/DCS1800/PCS1900/UMTS2100/LTE2300/2500 bands for mobile handset application, is proposed. It occupies a no‐ground space of only 8 × 60 mm2 and wideband operation can be achieved by meandering or folding loop line and using matching circuits. The low frequency bands of GSM850/900 operation are achieved by the loop half‐wavelength mode and matching chip capacitor, which generates a dual‐resonance mode at 780 and 915 MHz. At around high frequency ranges, a one‐wavelength resonant mode of the entire loop at 1.66 GHz, and high‐order resonant modes of the left or the right side coupling loop are generated to cover the residual frequency bands. In addition, the proposed antenna is easily printed on dielectric surfaces of a low cost FR4 substrate, which makes it suitable for practical mobile applications. The proposed antenna is successfully simulated, fabricated, and measured comparing to simulated results to validate the design. [ABSTRACT FROM AUTHOR]

• The LSNN method is proved to be quasi-optimal in the V β norm. • It approximates discontinuous solutions with the least DoFs but without oscillation. • The model continuation provides a good initialization for training. • Value of the least-squares functional is a good error estimator. This paper studies least-squares ReLU neural network method for solving the linear advection-reaction problem with discontinuous solution. The method is a discretization of an equivalent least-squares formulation in the set of neural network functions with the ReLU activation function. The method is capable of approximating the discontinuous interface of the underlying problem automatically through the free hyper-planes of the ReLU neural network and, hence, outperforms mesh-based numerical methods in terms of the number of degrees of freedom. Numerical results of some benchmark test problems show that the method can not only approximate the solution with the least number of parameters, but also avoid the common Gibbs phenomena along the discontinuous interface. Moreover, a three-layer ReLU neural network is necessary and sufficient in order to well approximate a discontinuous solution with an interface in R 2 that is not a straight line. [ABSTRACT FROM AUTHOR]

• The vaccination strategies analysis from mandatory to voluntary vaccination. • Establishes a SEIRV model considering birth rate and vaccination rate. • The evolutionary analysis of vaccination on heterogeneous network. • A simulation experiment is conducted using the SARS-Cov-2 pandemic data. Nowadays, vaccination is the most effective way to control the epidemic spreading. In this paper, an epidemic SEIRV (susceptible-exposed-infected-removed -vaccinated) model and an evolutionary game model are established to analyze the difference between mandatory vaccination method and voluntary vaccination method on heterogeneous networks. Firstly, we divide the population into four categories, including susceptible individuals, exposed individuals, infected individuals and removed individuals. Based on the mean field approximation theory, differential equations are developed to characterize the changes of the proportions of the four groups over time under mandatory vaccination. Then through the analysis of the differential equations, the disease-free equilibrium point (DFE) and the endemic disease equilibrium point (EDE) are obtained. Also, the basic reproduction number is obtained by the next-generation matrix method and the stability analysis of the equilibrium points is performed. Next, by considering factors such as vaccination cost, treatment cost and government subsidy rate, differential equations are established to represent the change of vaccination rate over time. By analyzing the final vaccination coverage rate, we can get the minimum vaccination cost to make infectious disease disappear. Finally, the Monte Carlo method is used for numerical simulation to verify the results obtained from the theoretical analysis. Using the SARS-Cov-2 pandemic data from Wuhan, China, the experimental results show that when the effectiveness rate of vaccination is 0.75, the vaccination cost is not higher than 0.886 so that the vaccination strategy can be spread among the population. If mandatory vaccination is adopted, the minimum vaccination rate is 0.146. [ABSTRACT FROM AUTHOR]

Natural-language based knowledge representations borrow their expressiveness from the semantics of language. One such knowledge representation technique is Latent semantic analysis (LSA), a statistical, corpus-based method for representing knowledge. It has been successfully used in a variety of applications including intelligent tutoring systems, essay grading and coherence metrics. The advantage of LSA is that it is efficient in representing world knowledge without the need for manual coding of relations and that it has in fact been considered to simulate aspects of human knowledge representation. An overview of LSA applications will be given, followed by some further explorations of the use of LSA. These explorations focus on the idea that the power of LSA can be amplified by considering semantic fields of text units instead of pairs of text units. Examples are given for semantic networks, category membership, typicality, spatiality and temporality, showing new evidence for LSA as a mechanism for knowledge representation. The results of such tests show that while the mechanism behind LSA is unique, it is flexible enough to replicate results in different corpora and languages. [ABSTRACT FROM AUTHOR]

Abstract: In [Z. Cai, S. Kim, A finite element method using singular functions for the Poisson equation: corner singularities, SIAM J. Numer. Anal. 39 (2001) 286–299], we proposed a new finite element method to compute singular solutions of Poisson equations on a polygonal domain with re-entrant angles. Singularities are eliminated and only the regular part of the solution that is in H 2 is computed. The stress intensity factor and the solution can be computed as a post-processing step. This method is extended to problems with crack singularities and to a higher-order method for smooth data in [Z. Cai, S. Kim, G. Woo, A finite element method using singular functions for the Poisson equation: crack singularities, Numer. Linear Algebra Appl. 9 (2002) 445–455]. In this paper, we study the Poisson equation with mixed boundary conditions. Examples with various singular points and numerical results are presented. [Copyright &y& Elsevier]

When respondents do not understand the meaning of a survey question, they will not supply valid and reliable answers. Survey methodologists should therefore benefit from computer tools and other analytical schemes that help them identify problems with questions with respect to comprehension difficulty. We developed a Web facility called Question Understanding Aid (QUAID; www.psyc.memphis.edu/quaid.html) that assists survey methodologists in identifying problems with the wording, syntax, and semantics of questions on questionnaires. The survey methodologist enters the question into the Web facility, along with any context information and answer alternatives that accompany the question. QUAID quickly returns a list of potential problems with question comprehension, including unfamiliar technical terms, vague or imprecise relative terms, vague or ambiguous noun phrases, complex syntax, and working memory overload. This article describes QUAID and some empirical studies that have assessed the validity and utility of QUAID's critiques of questions. The output of QUAID was compared with the judgments of experts in language, discourse, and cognition during the development of the tool. In one evaluation, expert survey methodologists critiqued and revised problematic questions, whereas in a second evaluation survey methodologists evaluated the quality of original problematic questions, questions revised with the assistance of QUAID, and questions revised without QUAID. In a third evaluation, eye-tracking data were collected while respondents read questions on a computer screen and answered questions aloud. Respondents had a tendency to give up processing difficult questions too early (called an early exit), which potentially threatens the validity of the respondents' answers. Survey methodologists are encouraged to use QUAID and further evaluate its validity and utility. [ABSTRACT FROM AUTHOR]

Abstract: Efficient operation of 4 F 3/2–4 I 9/2 transition in diode-end-pumped Nd:YAG lasers at 946nm is reported. At incident pump power of 23.9W, up to 6.6W continuous-wave (cw) output power has been obtained with a conventional non-composite Nd:YAG rod, leading to 27.1% optical-to-optical efficiency and 29.8% slop efficiency. To our knowledge, this is the highest value of LD pumped Nd:YAG 946nm laser with a conversional Nd:YAG rod as gain medium. By intracavity frequency-doubling with an LBO crystal in a compact three-element resonator, cw output power of 1.05W in the blue spectral range at 473nm is achieved with 8% optical-to-optical conversion efficiency versus incident pump power of 13.1W. The instability of the blue laser is less than 1% during half an hour. [Copyright &y& Elsevier]

Aero‐engine assembly has a wide range of indicators, and finding critical assembly elements to guide assembly and troubleshooting is essential in improving the reliability and safety of aero‐engine. In order to identify critical elements in aero‐engine assembly components, this study aims to establish a two‐stage hybrid feature selection model, namely, FSBP approach, which integrated filter method and particle swarm algorithm with Bayesian optimization. Specifically, individual filter feature selection methods are compared to select a relatively effective method to reduce the data dimensions and ensure the quality of the initial subset. Then, the particle swarm algorithm combined with Bayesian optimization obtains a subset of features in the second stage that are more suitable for predictive models with more robust classification prediction capability. The algorithm is successfully applied to real aero‐engine assembly and trial test datasets, and the experimental results show that our proposed two‐stage hybrid feature selection model outperforms other benchmark methods. [ABSTRACT FROM AUTHOR]

Based on the Helmholtz decomposition of the transverse shear strain, Brezzi and Fortin in [7] introduced a three-stage algorithm for approximating the Reissner-Mindlin plate model with clamped boundary conditions and established uniform error estimates in the plate thickness. The first and third stages involve approximating two simple Poisson equations and the second stage approximates a perturbed Stokes equation. Instead of using the mixed finite element method which is subject to the ‘infsup’ condition, we consider a least-squares finite element approximation to such a perturbed Stokes equation. By introducing a new independent vector variable and associated div equation, we are able to establish the ellipticity and continuity of the homogeneous least-squares functional in an H1 product norm appropriately weighted by the thickness. This immediately yields optimal discretization error estimates for finite element spaces in this norm which are uniform in the thickness. We show that the resulting algebraic equations can be uniformly well preconditioned by well-known techniques in the thickness. The Reissner-Mindlin model with pure traction boundary condition is also studied. Finally, we consider an alternative least-squares formulation for the perturbed Stokes equation by introducing an independent scalar variable. Copyright © 1999 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]