Antiviral software systems (AVSs) have problems in identifying polymorphic variants of viruses without explicit signatures for such variants. Alignment-based techniques from bioinformatics may provide a novel way to g...Antiviral software systems (AVSs) have problems in identifying polymorphic variants of viruses without explicit signatures for such variants. Alignment-based techniques from bioinformatics may provide a novel way to generate signatures from consensuses found in polymorphic variant code. We demonstrate how multiple sequence alignment supplemented with gap penalties leads to viral code signatures that generalize successfully to previously known polymorphic variants of JS. Cassandra virus and previously unknown polymorphic variants of W32.CTX/W32.Cholera and W32.Kitti viruses. The implications are that future smart AVSs may be able to generate effective signatures automatically from actual viral code by varying gap penalties to cover for both known and unknown polymorphic variants.展开更多
The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss i...The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.展开更多
Because governments have introduced policies involving incentives and penalties to promote the recycling of plastic waste,it is important to understand the impact of such incentives and penalties on the willingness of...Because governments have introduced policies involving incentives and penalties to promote the recycling of plastic waste,it is important to understand the impact of such incentives and penalties on the willingness of stakeholders to participate.In this study,government is included as a player,alongside waste collectors and recyclers,in a tripartite evolutionary game model of plastic waste recycling.The study explores the evolutionary equilibrium and performs a simulation analysis to elucidate the effect of government incentives and penalties on the willingness of other players to participate in recycling.Three conclusions are drawn from this research.First,an increase in incentives or in penalties increases the probability that collectors and recyclers will participate in the recycling process.Second,policy support incentives encourage collectors and recyclers to participate in plastic waste recycling earlier than subsidy incentives do.Finally,recyclers are more sensitive than collectors to government-imposed penalties.展开更多
The extant literature offers extensive support for the significant role played by institutions in financial markets,but implicit regulation and monitoring have yet to be examined.This study fills this void in the lite...The extant literature offers extensive support for the significant role played by institutions in financial markets,but implicit regulation and monitoring have yet to be examined.This study fills this void in the literature by employing unique Chinese datasets to explore the implicit regulation and penalties imposed by the Chinese government in regulating the initial public offering(IPO) market.Of particular interest are the economic consequences of underwriting IPO deals for client firms that violate regulatory rules in China's capital market.We provide evidence to show that the associated underwriters' reputations are impaired and their market share declines.We further explore whether such negative consequences result from a market disciplinary mechanism or a penalty imposed by the government.To analyze the possibility of a market disciplinary mechanism at work,we investigate(1) the market reaction to other client firms whose IPO deals were underwritten by underwriters associated with a violation at the time the violation was publicly disclosed and(2) the under-pricing of IPO deals undertaken by these underwriters after such disclosure.To analyze whether the government imposes an implicit penalty,we examine the application processing time for future IPO deals underwritten by the associated underwriters and find it to be significantly longer than for IPO deals underwritten by other underwriters.Overall,there is little evidence to suggest that the market penalizes underwriters for the rule-violating behavior of their client firms in China.Instead,the Chinese government implicitly penalizes them by imposing more stringent criteria on and lengthening the processing time of the IPO deals they subsequently underwrite.展开更多
The safety of risers in hang-off states is a vital challenge in offshore oil and gas engineering.A new hang-off system installed on top of risers is proposed for improving the security of risers.This approach leads to...The safety of risers in hang-off states is a vital challenge in offshore oil and gas engineering.A new hang-off system installed on top of risers is proposed for improving the security of risers.This approach leads to a challenging problem:coupling the dynamics of risers with a new hang-off system combined with multiple structures and complex constraints.To accurately analyze the dynamic responses of the coupled system,a coupled dynamic model is established based on the Euler-Bernoulli beam-column theory and penalty function method.A comprehensive analysis method is proposed for coupled dynamic analysis by combining the finite element method and the Newmarkβmethod.An analysis program is also developed in MATLAB for dynamic simulation.The simulation results show that the dynamic performances of the risers at the top part are significantly improved by the new hang-off system,especially the novel design,which includes the centralizer and articulation joint.The bending moment and lateral deformation of the risers at the top part decrease,while the hang-off joint experiences a great bending moment at the bottom of the lateral restraint area which requires particular attention in design and application.The platform navigation speed range under the safety limits of risers expands with the new hang-off system in use.展开更多
Nowadays manufacturers are facing fierce challenge.Apart from the products,providing customers with multiple maintenance options in the service contract becomes more popular,since it can help to improve customer satis...Nowadays manufacturers are facing fierce challenge.Apart from the products,providing customers with multiple maintenance options in the service contract becomes more popular,since it can help to improve customer satisfaction,and ultimately promote sales and maximize profit for the manufacturer.By considering the combinations of corrective maintenance and preventive maintenance,totally three types of maintenance service contracts are designed.Moreover,attractive incentive and penalty mechanisms are adopted in the contracts.On this basis,Nash non-cooperative game is applied to analyze the revenue for both the manufacturer and customers,and so as to optimize the pricing mechanism of maintenance service contract and achieve a win-win situation.Numerical experiments are conducted.The results show that by taking into account the incentive and penalty mechanisms,the revenue can be improved for both the customers and manufacturer.Moreover,with the increase of repair rate and improvement factor in the preventive maintenance,the revenue will increase gradually for both the parties.展开更多
Making profits is not the only goal for central SOE executives as they face repercussions for failure to reduce emissions The State-Owned Assets Supervisionand Administration Commission(SASAC),which supervises
If you’re thinking of slapping someone in public,not only do you have anger issues to deal with,but now you could be on the receiving end of a hefty fine. In a bid to clamp down on public vio- lence,a growing number ...If you’re thinking of slapping someone in public,not only do you have anger issues to deal with,but now you could be on the receiving end of a hefty fine. In a bid to clamp down on public vio- lence,a growing number of grassroots police authorities around China are experimenting with a new fistfight prevention policy. Simply put,the core of the policy is to com- pile a so-called"fistfight cost sheet,"which lists the specific punishment for those who involve themselves in fistfights as opposed to previous relevant statutes,such as the Law on Administrative Penalty,which are vague. Police are hoping this initiative will help to curb public violence due to its financial deterrent.展开更多
This article addresses the problem of scheduling n jobs with a common due date on a machine subject to stochastic breakdowns to minimize absolute early-tardy penalties.We investigate the problem under the conditions t...This article addresses the problem of scheduling n jobs with a common due date on a machine subject to stochastic breakdowns to minimize absolute early-tardy penalties.We investigate the problem under the conditions that the uptimes follow an exponential distribution,and the objective measure in detail is to minimize the expected sum of the absolute deviations of completion times from the common due date.We proceed to study in two versions (the downtime follows an exponential distribution or is a constant entailed for the repeat model job),one of which is the so-called preempt- resume version,the other of which is the preempt-repeat version.Three terms of work have been done.(i)Formulations and Preliminaries.A few of necessary definitions,relations and basic facts are established.In particular,the conclusion that the expectation of the absolute deviation of the completion time about a job with deterministic processing time t from a due date is a semi-V-shape function in t has been proved.(ii) Properties of Optimal Solutions.A few characteristics of optimal solutions are established.Most importantly,the conclusion that optimal solutions possess semi-V- shape property has been proved.(iii) Algorithm.Some computing problems on searching for optimal solutions are discussed.展开更多
According to the risk management and reputation insurance theory of corporate social responsibility, corporate donations can help a company to repair its reputation after a crisis.This study uses a propensity score ma...According to the risk management and reputation insurance theory of corporate social responsibility, corporate donations can help a company to repair its reputation after a crisis.This study uses a propensity score matching–difference in difference(PSM + DID) methodology to investigate the charitable donation activities of companies that have been subject to regulatory penalties.The analysis of a sample of A-share listed companies in the 2004–2016 period shows that companies significantly increase their charitable donations after regulatory penalties, but this effect weakens over time.Further analysis reveals that non-state-owned companies, companies with higher ownership concentrations, and companies receiving severer penalties are more motivated to make donations after regulatory penalties.By studying the reputation repair behavior of companies that have been subject to regulatory penalties, this study offers further support for the risk management and reputation insurance theory of corporate social responsibility.It also enriches our understanding of companies’ active responses to regulatory penalties and provides insights into companies’ motives for making charitable donations.展开更多
A nonconforming rectangular finite element method is proposed to solve a fluid structure interaction problem characterized by the Darcy-Stokes-Brinkman Equation with discontinuous coefficients across the interface of ...A nonconforming rectangular finite element method is proposed to solve a fluid structure interaction problem characterized by the Darcy-Stokes-Brinkman Equation with discontinuous coefficients across the interface of different structures.A uniformly stable mixed finite element together with Nitsche-type matching condi-tions that automatically adapt to the coupling of different sub-problem combinations are utilized in the discrete algorithm.Compared with other finite element methods in the literature,the new method has some distinguished advantages and features.The Boland-Nicolaides trick is used in proving the inf-sup condition for the multi-domain discrete problem.Optimal error estimates are derived for the coupled prob-lem by analyzing the approximation errors and the consistency errors.Numerical examples are also provided to confirm the theoretical results.展开更多
Plant diseases have become a challenging threat in the agricultural field.Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early.Howeve...Plant diseases have become a challenging threat in the agricultural field.Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early.However,deep learning entails extensive data for training,and it may be challenging to collect plant datasets.Even though plant datasets can be collected,they may be uneven in quantity.As a result,the problem of classification model overfitting arises.This study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available data.First,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image quality.Then,a gradient penalty mechanism was added to the loss function to improve the training stability of the model.Experiments show that the proposed method can best improve the recognition accuracy of the classifier with the doubled dataset.On AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the experiment.Moreover,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original dataset.Furthermore,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this approach.The general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.展开更多
With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detec...With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.展开更多
Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mob...Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mobile Adhoc system management,on the other hand,requires further testing and improvements in terms of security.Traditional routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two nodes.The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network.It is achieved by changing the residual energy of each network node.Also,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are proposed.Moreover,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters.The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.展开更多
A theoretical study based on the Penalty factor(PF)method by Cavallini et al.is conducted to show that the pressure drop occurring in a wire-on-tube heat exchanger can be converted into a temperature difference for tw...A theoretical study based on the Penalty factor(PF)method by Cavallini et al.is conducted to show that the pressure drop occurring in a wire-on-tube heat exchanger can be converted into a temperature difference for two types of refrigerants R-134a and R-600a typically used for charging refrigerators and freezers.The following conditions are considered:stratified or stratified-wavyflow condensation occurring inside the smooth tube of a wire-on-tube condenser with diameter 3.25,4.83,and 6.299 mm,condensation temperatures 35℃,45℃,and 54.4℃ and cover refrigerant massflow rate spanning the interval from 1 to 7 kg/hr.The results show that the PF variation is not linear with vapor quality and attains a maximum when the vapor quality is 0.2 and 0.18 for the R-134a and R-600a refrigerants,respectively.The PF increases with the refrigerant massflow rate if the inner diameter and saturation temperature constant,and it decreases on increasing the inner diameter to 6.299 mm for constant refrigerant massflow rate and saturation temperature.The PF for R-600a is higher than that for R-134a due to the lower saturation pressure in thefirst case.Furthermore,a stratifiedflow produces higher PF in comparison to the annularflow due to the effect of the surface tension.展开更多
Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals.However,the training of Generative Adversarial Networks has such problems as con...Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals.However,the training of Generative Adversarial Networks has such problems as convergence difficulty,model collapse,etc.In this work,an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed,and some improvements have been made in order to get faster convergence speed and better generated speech quality.Specifically,in the generator coding part,each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales;a gated linear unit is introduced to alleviate the vanishing gradient problem with the increase of network depth;the gradient penalty of the discriminator is replaced with spectral normalization to accelerate the convergence rate of themodel;a hybrid penalty termcomposed of L1 regularization and a scale-invariant signal-to-distortion ratio is introduced into the loss function of the generator to improve the quality of generated speech.The experimental results on both TIMIT corpus and Tibetan corpus show that the proposed model improves the speech quality significantly and accelerates the convergence speed of the model.展开更多
Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced...Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.展开更多
文摘Antiviral software systems (AVSs) have problems in identifying polymorphic variants of viruses without explicit signatures for such variants. Alignment-based techniques from bioinformatics may provide a novel way to generate signatures from consensuses found in polymorphic variant code. We demonstrate how multiple sequence alignment supplemented with gap penalties leads to viral code signatures that generalize successfully to previously known polymorphic variants of JS. Cassandra virus and previously unknown polymorphic variants of W32.CTX/W32.Cholera and W32.Kitti viruses. The implications are that future smart AVSs may be able to generate effective signatures automatically from actual viral code by varying gap penalties to cover for both known and unknown polymorphic variants.
文摘The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.
基金the National Natural Science Foundation of China(Grant No:71532015).
文摘Because governments have introduced policies involving incentives and penalties to promote the recycling of plastic waste,it is important to understand the impact of such incentives and penalties on the willingness of stakeholders to participate.In this study,government is included as a player,alongside waste collectors and recyclers,in a tripartite evolutionary game model of plastic waste recycling.The study explores the evolutionary equilibrium and performs a simulation analysis to elucidate the effect of government incentives and penalties on the willingness of other players to participate in recycling.Three conclusions are drawn from this research.First,an increase in incentives or in penalties increases the probability that collectors and recyclers will participate in the recycling process.Second,policy support incentives encourage collectors and recyclers to participate in plastic waste recycling earlier than subsidy incentives do.Finally,recyclers are more sensitive than collectors to government-imposed penalties.
基金supported by the National Social Science Fund(Grant No.08CJY009)the National Natural Science Fund(Grant Nos.70732002 and 70602011)+1 种基金support from the IAPHD Project of Nanjing Universitythe Institute of Accounting and Finance of Shanghai University of Finance and Economics,Research Project 985 of the Institute of Economic Transition and Development of Nanjing University,and the discussion at the 2009 winter seminar at City University of Hong Kong
文摘The extant literature offers extensive support for the significant role played by institutions in financial markets,but implicit regulation and monitoring have yet to be examined.This study fills this void in the literature by employing unique Chinese datasets to explore the implicit regulation and penalties imposed by the Chinese government in regulating the initial public offering(IPO) market.Of particular interest are the economic consequences of underwriting IPO deals for client firms that violate regulatory rules in China's capital market.We provide evidence to show that the associated underwriters' reputations are impaired and their market share declines.We further explore whether such negative consequences result from a market disciplinary mechanism or a penalty imposed by the government.To analyze the possibility of a market disciplinary mechanism at work,we investigate(1) the market reaction to other client firms whose IPO deals were underwritten by underwriters associated with a violation at the time the violation was publicly disclosed and(2) the under-pricing of IPO deals undertaken by these underwriters after such disclosure.To analyze whether the government imposes an implicit penalty,we examine the application processing time for future IPO deals underwritten by the associated underwriters and find it to be significantly longer than for IPO deals underwritten by other underwriters.Overall,there is little evidence to suggest that the market penalizes underwriters for the rule-violating behavior of their client firms in China.Instead,the Chinese government implicitly penalizes them by imposing more stringent criteria on and lengthening the processing time of the IPO deals they subsequently underwrite.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52271300,52071337,and 51809279)the National Key Research and Development Program of China(Grant No.2022YFC2806501)the High-tech Ship Research Projects Sponsored by MIIT(Grant No.CBG2N21-4-2-5).
文摘The safety of risers in hang-off states is a vital challenge in offshore oil and gas engineering.A new hang-off system installed on top of risers is proposed for improving the security of risers.This approach leads to a challenging problem:coupling the dynamics of risers with a new hang-off system combined with multiple structures and complex constraints.To accurately analyze the dynamic responses of the coupled system,a coupled dynamic model is established based on the Euler-Bernoulli beam-column theory and penalty function method.A comprehensive analysis method is proposed for coupled dynamic analysis by combining the finite element method and the Newmarkβmethod.An analysis program is also developed in MATLAB for dynamic simulation.The simulation results show that the dynamic performances of the risers at the top part are significantly improved by the new hang-off system,especially the novel design,which includes the centralizer and articulation joint.The bending moment and lateral deformation of the risers at the top part decrease,while the hang-off joint experiences a great bending moment at the bottom of the lateral restraint area which requires particular attention in design and application.The platform navigation speed range under the safety limits of risers expands with the new hang-off system in use.
基金supported by the National Natural Science Foundation of China(71671035)。
文摘Nowadays manufacturers are facing fierce challenge.Apart from the products,providing customers with multiple maintenance options in the service contract becomes more popular,since it can help to improve customer satisfaction,and ultimately promote sales and maximize profit for the manufacturer.By considering the combinations of corrective maintenance and preventive maintenance,totally three types of maintenance service contracts are designed.Moreover,attractive incentive and penalty mechanisms are adopted in the contracts.On this basis,Nash non-cooperative game is applied to analyze the revenue for both the manufacturer and customers,and so as to optimize the pricing mechanism of maintenance service contract and achieve a win-win situation.Numerical experiments are conducted.The results show that by taking into account the incentive and penalty mechanisms,the revenue can be improved for both the customers and manufacturer.Moreover,with the increase of repair rate and improvement factor in the preventive maintenance,the revenue will increase gradually for both the parties.
文摘Making profits is not the only goal for central SOE executives as they face repercussions for failure to reduce emissions The State-Owned Assets Supervisionand Administration Commission(SASAC),which supervises
文摘If you’re thinking of slapping someone in public,not only do you have anger issues to deal with,but now you could be on the receiving end of a hefty fine. In a bid to clamp down on public vio- lence,a growing number of grassroots police authorities around China are experimenting with a new fistfight prevention policy. Simply put,the core of the policy is to com- pile a so-called"fistfight cost sheet,"which lists the specific punishment for those who involve themselves in fistfights as opposed to previous relevant statutes,such as the Law on Administrative Penalty,which are vague. Police are hoping this initiative will help to curb public violence due to its financial deterrent.
基金the National Natural Science Foundation of China (Grant No.10471096)
文摘This article addresses the problem of scheduling n jobs with a common due date on a machine subject to stochastic breakdowns to minimize absolute early-tardy penalties.We investigate the problem under the conditions that the uptimes follow an exponential distribution,and the objective measure in detail is to minimize the expected sum of the absolute deviations of completion times from the common due date.We proceed to study in two versions (the downtime follows an exponential distribution or is a constant entailed for the repeat model job),one of which is the so-called preempt- resume version,the other of which is the preempt-repeat version.Three terms of work have been done.(i)Formulations and Preliminaries.A few of necessary definitions,relations and basic facts are established.In particular,the conclusion that the expectation of the absolute deviation of the completion time about a job with deterministic processing time t from a due date is a semi-V-shape function in t has been proved.(ii) Properties of Optimal Solutions.A few characteristics of optimal solutions are established.Most importantly,the conclusion that optimal solutions possess semi-V- shape property has been proved.(iii) Algorithm.Some computing problems on searching for optimal solutions are discussed.
基金financial support from the China National Social Science Foundation Key Research Project (Project No.17ZDA086)
文摘According to the risk management and reputation insurance theory of corporate social responsibility, corporate donations can help a company to repair its reputation after a crisis.This study uses a propensity score matching–difference in difference(PSM + DID) methodology to investigate the charitable donation activities of companies that have been subject to regulatory penalties.The analysis of a sample of A-share listed companies in the 2004–2016 period shows that companies significantly increase their charitable donations after regulatory penalties, but this effect weakens over time.Further analysis reveals that non-state-owned companies, companies with higher ownership concentrations, and companies receiving severer penalties are more motivated to make donations after regulatory penalties.By studying the reputation repair behavior of companies that have been subject to regulatory penalties, this study offers further support for the risk management and reputation insurance theory of corporate social responsibility.It also enriches our understanding of companies’ active responses to regulatory penalties and provides insights into companies’ motives for making charitable donations.
基金supported by the US NSF grant DMS-1522768,CNFS grants.11371199,11471166.
文摘A nonconforming rectangular finite element method is proposed to solve a fluid structure interaction problem characterized by the Darcy-Stokes-Brinkman Equation with discontinuous coefficients across the interface of different structures.A uniformly stable mixed finite element together with Nitsche-type matching condi-tions that automatically adapt to the coupling of different sub-problem combinations are utilized in the discrete algorithm.Compared with other finite element methods in the literature,the new method has some distinguished advantages and features.The Boland-Nicolaides trick is used in proving the inf-sup condition for the multi-domain discrete problem.Optimal error estimates are derived for the coupled prob-lem by analyzing the approximation errors and the consistency errors.Numerical examples are also provided to confirm the theoretical results.
文摘Plant diseases have become a challenging threat in the agricultural field.Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early.However,deep learning entails extensive data for training,and it may be challenging to collect plant datasets.Even though plant datasets can be collected,they may be uneven in quantity.As a result,the problem of classification model overfitting arises.This study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available data.First,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image quality.Then,a gradient penalty mechanism was added to the loss function to improve the training stability of the model.Experiments show that the proposed method can best improve the recognition accuracy of the classifier with the doubled dataset.On AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the experiment.Moreover,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original dataset.Furthermore,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this approach.The general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.
基金This research was funded by Innovation and Entrepreneurship Training Program for College Students in Hunan Province in 2022(3915).
文摘With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.
文摘Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mobile Adhoc system management,on the other hand,requires further testing and improvements in terms of security.Traditional routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two nodes.The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network.It is achieved by changing the residual energy of each network node.Also,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are proposed.Moreover,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters.The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.
文摘A theoretical study based on the Penalty factor(PF)method by Cavallini et al.is conducted to show that the pressure drop occurring in a wire-on-tube heat exchanger can be converted into a temperature difference for two types of refrigerants R-134a and R-600a typically used for charging refrigerators and freezers.The following conditions are considered:stratified or stratified-wavyflow condensation occurring inside the smooth tube of a wire-on-tube condenser with diameter 3.25,4.83,and 6.299 mm,condensation temperatures 35℃,45℃,and 54.4℃ and cover refrigerant massflow rate spanning the interval from 1 to 7 kg/hr.The results show that the PF variation is not linear with vapor quality and attains a maximum when the vapor quality is 0.2 and 0.18 for the R-134a and R-600a refrigerants,respectively.The PF increases with the refrigerant massflow rate if the inner diameter and saturation temperature constant,and it decreases on increasing the inner diameter to 6.299 mm for constant refrigerant massflow rate and saturation temperature.The PF for R-600a is higher than that for R-134a due to the lower saturation pressure in thefirst case.Furthermore,a stratifiedflow produces higher PF in comparison to the annularflow due to the effect of the surface tension.
基金supported by the National Science Foundation under Grant No.62066039.
文摘Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals.However,the training of Generative Adversarial Networks has such problems as convergence difficulty,model collapse,etc.In this work,an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed,and some improvements have been made in order to get faster convergence speed and better generated speech quality.Specifically,in the generator coding part,each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales;a gated linear unit is introduced to alleviate the vanishing gradient problem with the increase of network depth;the gradient penalty of the discriminator is replaced with spectral normalization to accelerate the convergence rate of themodel;a hybrid penalty termcomposed of L1 regularization and a scale-invariant signal-to-distortion ratio is introduced into the loss function of the generator to improve the quality of generated speech.The experimental results on both TIMIT corpus and Tibetan corpus show that the proposed model improves the speech quality significantly and accelerates the convergence speed of the model.
基金supported by National Natural Science Foundation of China(Grant Nos.52279137,52009090).
文摘Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.
