Exploring the Effects of Gap-Penalties in Sequence-Alignment Approach to Polymorphic Virus Detection

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.

Should the Harsh Penalties on Drunk Driving Be loosened?

Recently,reform on charges against dangerous driving has stirred up debate among the public.A new stipulation by China’s Supreme People’s court which took effect on May 1 granted judges the discretion to decide whether someone caught driving under the influence of alcohol should be held criminal responsible by taking into account how drunk the person is,road conditions and the seriousness of damages.

Public Security Penalties Law Respects, Protects Human Rights

On August 28, 2005, China＇s highest legislature adopted the Law on Penalties in Respect to Public Security Management （hereinafterrefen＇ed to as the Public Security Penalties Law for short）. The Law, adopted at the 17th meeting of the Standing Committee of the Tenth National People＇s Congress, shall become effective for implementation on March 1, 2006 to replace the Regulations on Penalties in Respect to Public： Security Management （Herein after referred to as the Public Security Penalties Regulations for short） that has been in practice for 18 years.

Yellow Light Penalties

A new traffic regulation that penalizes drivers for running yellow lights has stirred up contro- versy since it took effect on January 1, 2013. Under the new regulation, the strictest to date in China, drivers running a yellow traffic light will have six points deducted from the 12 points allocated to their driver＇s licenses. Previously, drivers were docked only three points for driv- ing through red light.

Machine learning model based on non-convex penalized huberized-SVM

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.

CALCULATION OF PENALTIES IN ALGORITHM OF MIXEDINTEGER PROGRAMMING SOLVING WITH REVISED DUALSIMPLEX METHOD FOR BOUNDED VARIABLES

The branch-and-bound method with the revised dual simplex for bounded variables is very effective in solving relatively large-size integer linear programming problems. This paper, based on the general forms of the penalties by Beale and Small and the stronger penalties by Tomlin, describes the modifications of these penalties used for the method of bounded variables. The same examples from Petersen are taken and the satisfactory results are shown in comparison with those obtained by Tomlin.

The impact of government incentives and penalties on willingness to recycle plastic waste: An evolutionary game theory perspective

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.

Securities regulation and implicit penalties

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.

Dynamic Analysis of A Deepwater Drilling Riser with A New Hang-off System

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.

Design and pricing of maintenance service contract based on Nash non-cooperative game approach

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.

Low-Carbon Prizes And Penalties

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

Will Harsher Penalties For Fistfights Prevent Public Violence?

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.

THE DISTRIBUTION-FREE NEWSVENDOR PROBLEM WITH BALKING AND PENALTIES FOR BALKING AND STOCKOUT

This paper investigates the ordering policy for the newsvendor problem with customer balking and penalties for balking and stockout. Our analysis is based on the assumption that only the mean and the variance of the demand distribution are known. In contrast to the existing research, we provide a new tradeoff tool as a replacement of the traditional one to weigh the holding cost and the goodwill cost segment： the balking penalty cost and the stockout penalty cost. Specifically, in addition to the stockout penalty, we also introduce the balking penalty, provide a new proof of the optimality of robust ordering policy to guarantee that the lower bound of expected profit obtained by us is tight, and get an robust optimal order quantity which is an exact solution but not an approximate one as before. Numerical experiments are conducted to illustrate the effect of penalties for balking and stockout.

Scheduling jobs on a machine subject to stochastic breakdowns to minimize absolute early-tardy penalties

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.

Reputation repair and corporate donations:An investigation of responses to regulatory penalties

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.

ADJUDICATING THE REGULATORY PENALTIES AGAINST NONPROFESSIONAL TAXI SERVICES IN CHINA AND THE EUROPEAN UNION

This paper provides an overview of judicial decisions on lawsuits against regulatory penalties imposed on nonprofessional taxi drivers and ride-hailing platform operators in China and the European Union （especially Germany）. Despite strikingly different facts in these cases, courts in both China and the EU are frequently called upon to rule on similar legal issues, including the applicability of old regulatory rules to new forms of transport services, the regulatory bar for the operation of emerging transport models, and the proper intensity of competition in taxi markets. The comparison of such cases suggests that for deciding the regulatory schemes of the innovative economy of transport services, the judicial system is not better suited than the regulatory system, especially the regulatory authority of the central government. Moreover, an experimental regulatory approach with minimum standards is arguably a feasible option that can fit with the emerging nature of innovative businesses.

A Uniformly Stable Nonconforming FEM Based on Weighted Interior Penalties for Darcy-Stokes-Brinkman Equations

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.

Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

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.

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

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.

Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks

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.