On the Dilemma of Contemporary Liberal Theory of Moral Rights for Penalty Justification——Exemplified by the Right to Personal Liberty

Contemporary liberal theory on moral rights argues that moral rights associated with personal liberty constitute a strong constraint on the boundaries of state power.Therefore,the core issue of the penalty justification is not the purpose of the penalty,but the reason for the penalty to refrain from infringing on the moral rights of individuals.In order to justify the penal system,scholars have explored solutions such as limiting the content of rights,waiving rights,and finally rights forfeiture.However,the concept of rights forfeiture cannot be reasonably integrated into the framework of the liberal theory of moral rights.The failure of these attempts stems from the patchwork understanding of rights presupposed by the liberal theory of moral rights.There is another systematic way of understanding rights that offers a better justification.Individual rights are not an independent non-derivative moral justification,and both individual rights and the penal power of the state are only part of a specific(realistic or ideal)system of rules that collectively serve certain values.The real question of penalty justification is not why the punishment does not infringe on the moral rights of individuals,but whether the overall institutional arrangements,including the penal system,are justifiable for all citizens,including the punished.

Essential oil content and composition of Indian sandalwood (Santalum album) in Sri Lanka

Santalum album （Indian Sandalwood） is found in the mountainous regions of the intermediate zone of Sri Lanka. Few studies have been conducted on sandalwood ecology in this region, and ours is the first recorded study of essential oil content and chemical composition of heartwood. We harvested two trees with State permission and took cross-sections for analysis. We demonstrated a difference in the heartwood formation and oil yield of the trees. The composition of the oil was found to be consistent between trees and along the trunk of the tree. Main aromatic compounds were santalols and other compounds are recorded in lesser quantities. Results of this study comply with the other published work on sandalwood elsewhere. This initial study on S. album in Sri Lanka provided promising results for the future of sandalwood agroforestry.

A DUAL-RELAX PENALTY FUNCTION APPROACH FOR SOLVING NONLINEAR BILEVEL PROGRAMMING WITH LINEAR LOWER LEVEL PROBLEM

The penalty function method, presented many years ago, is an important nu- merical method for the mathematical programming problems. In this article, we propose a dual-relax penalty function approach, which is significantly different from penalty func- tion approach existing for solving the bilevel programming, to solve the nonlinear bilevel programming with linear lower level problem. Our algorithm will redound to the error analysis for computing an approximate solution to the bilevel programming. The error estimate is obtained among the optimal objective function value of the dual-relax penalty problem and of the original bilevel programming problem. An example is illustrated to show the feasibility of the proposed approach.

Novel Discrete Particle Swarm Optimization Based on Huge Value Penalty for Solving Engineering Problem

For the purpose of solving the engineering constrained discrete optimization problem, a novel discrete particle swarm optimization（DPSO） is proposed. The proposed novel DPSO is based on the idea of normal particle swarm optimization（PSO）, but deals with the variables as discrete type, the discrete optimum solution is found through updating the location of discrete variable. To avoid long calculation time and improve the efficiency of algorithm, scheme of constraint level and huge value penalty are proposed to deal with the constraints, the stratagem of reproducing the new particles and best keeping model of particle are employed to increase the diversity of particles. The validity of the proposed DPSO is examined by benchmark numerical examples, the results show that the novel DPSO has great advantages over current algorithm. The optimum designs of the 100-1 500 mm bellows under 0.25 MPa are fulfilled by DPSO. Comparing the optimization results with the bellows in-service, optimization results by discrete penalty particle swarm optimization（DPPSO） and theory solution, the comparison result shows that the global discrete optima of bellows are obtained by proposed DPSO, and confirms that the proposed novel DPSO and schemes can be used to solve the engineering constrained discrete problem successfully.

NONLINEAR PROGRAMMING VIA AN EXACT PENALTY FUNCTION：CONVERGENCE RATE ANALYSIS

The algorithm proposed by T. F. Colemen and A. R. Conn is improved in this paper, and the improved algorithm can solve nonlinear programming problem with quality constraints. It is shown that the improved algorithm possesses global convergence, and under some conditions, it possesses locally supperlinear convergence.

THE RISK MODEL OF THE EXPECTED DISCOUNTED PENALTY FUNCTION WITH CONSTANT INTEREST FORCE

In this article, the expected discounted penalty function Фδ,α （u） with constant interest δ and ＂discounted factor＂ exp（-αTδ） is considered. As a result, the integral equation of Фδ,α （u） is derived and an exact solution for Фδ,α （0） is found. The relation between the joint density of the surplus immediately prior to ruin, and the deficit at ruin and the density of the surplus immediately prior to ruin is then obtained based on analytical methods.

Convergence of Online Gradient Method with Penalty for BP Neural Networks

Online gradient method has been widely used as a learning algorithm for training feedforward neural networks. Penalty is often introduced into the training procedure to improve the generalization performance and to decrease the magnitude of network weights. In this paper, some weight boundedness and deterministic con- vergence theorems are proved for the online gradient method with penalty for BP neural network with a hidden layer, assuming that the training samples are supplied with the network in a fixed order within each epoch. The monotonicity of the error function with penalty is also guaranteed in the training iteration. Simulation results for a 3-bits parity problem are presented to support our theoretical results.

General Exact Penalty Functions in Integer Programming

In this paper, the general exact penalty functions in integer programming were studied. The conditions which ensure the exact penalty property for the general penalty function with one penalty parameter were given and a general penalty function with two parameters was proposed.

Exactness of penalization for exact minimax penalty function method in nonconvex programming

The exact minimax penalty function method is used to solve a noncon- vex differentiable optimization problem with both inequality and equality constraints. The conditions for exactness of the penalization for the exact minimax penalty function method are established by assuming that the functions constituting the considered con- strained optimization problem are invex with respect to the same function η （with the exception of those equality constraints for which the associated Lagrange multipliers are negative these functions should be assumed to be incave with respect to η）. Thus, a threshold of the penalty parameter is given such that, for all penalty parameters exceeding this threshold, equivalence holds between the set of optimal solutions in the considered constrained optimization problem and the set of minimizer in its associated penalized problem with an exact minimax penalty function. It is shown that coercivity is not suf- ficient to prove the results.

Superconvergence of nonconforming finite element penalty scheme for Stokes problem using L^2 projection method

A modified penalty scheme is discussed for solving the Stokes problem with the Crouzeix-Raviart type nonconforming linear triangular finite element. By the L^2 projection method, the superconvergence results for the velocity and pressure are obtained with a penalty parameter larger than that of the classical penalty scheme. The numerical experiments are carried out to confirm the theoretical results.

Yield penalty of maize(Zea mays L.) under heat stress in different growth stages: A review

Maize(Zea mays L.) can exhibit yield penalties as a result of unfavorable changes to growing conditions. The main threat to current and future global maize production is heat stress. Maize may suffer from heat stress in all of the growth stages, either continuously or separately. In order to manage the impact of climate driven heat stress on the different growth stages of maize, there is an urgent need to understand the similarities and differences in how heat stress affects maize growth and yield in the different growth stages. For the purposes of this review, the maize growth cycle was divided into seven growth stages, namely the germination and seedling stage, early ear expansion stage, late vegetative growth stage before flowering, flowering stage, lag phase, effective grain-filling stage, and late grain-filling stage. The main focus of this review is on the yield penalty and the potential physiological changes caused by heat stress in these seven different stages. The commonalities and differences in heat stress related impacts on various physiological processes in the different growth stages are also compared and discussed. Finally, a framework is proposed to describe the main influences on yield components in different stages, which can serve as a useful guide for identifying management interventions to mitigate heat stress related declines in maize yield.

Method for electromagnetic detection satellites scheduling based on genetic algorithm with alterable penalty coefficient

The electromagnetic detection satellite （EDS） is a type of earth observation satellites （EOSs）. The Information collected by EDSs plays an important role in some fields, such as industry, science and military. The scheduling of EDSs is a complex combinatorial optimization problem. Current research mainly focuses on the scheduling of imaging satellites and SAR satellites, but little work has been done on the scheduling of EDSs for its specific characteristics. A multi-satellite scheduling model is established, in which the specific constrains of EDSs are considered, then a scheduling algorithm based on the genetic algorithm （GA） is proposed. To deal with the specific constrains of EDSs, a penalty function method is introduced. However, it is hard to determine the appropriate penalty coefficient in the penalty function. Therefore, an adaptive adjustment mechanism of the penalty coefficient is designed to solve the problem, as well as improve the scheduling results. Experimental results are used to demonstrate the correctness and practicability of the proposed scheduling algorithm.

EXPECTED DISCOUNTED PENALTY FUNCTION OF ERLANG(2) RISK MODEL WITH CONSTANT INTEREST

The purpose of this paper is to consider the expected value of a discounted penalty due at ruin in the Erlang（2） risk process under constant interest force. An integro-differential equation satisfied by the expected value and a second-order differential equation for the Laplace transform of the expected value are derived. In addition, the paper will present the recursive algorithm for the joint distribution of the surplus immediately before ruin and the deficit at ruin. Finally, by the differential equation, the defective renewal equation and the explicit expression for the expected value are given in the interest-free case.

THE CONVERGENCE OF APPROACH PENALTY FUNCTION METHOD FOR APPROXIMATE BILEVEL PROGRAMMING PROBLEM

In this paper, a new algorithm-approximate penalty function method is designed, which can be used to solve a bilevel optimization problem with linear constrained function. In this kind of bilevel optimization problem. the evaluation of the objective function is very difficult, so that only their approximate values can be obtained. This algorithm is obtained by combining penalty function method and approximation in bilevel programming. The presented algorithm is completely different from existing methods. That convergence for this algorithm is proved.

Credit risk evaluation using adaptive Lq penalty SVM with Gauss kernel

In order to improve the performance of support vector machine (SVM) applications in the field of credit risk evaluation, an adaptive Lq SVM model with Gauss kernel (ALqG-SVM) is proposed to evaluate credit risks. The non-adaptive penalty of the object function is extended to (0, 2] to increase classification accuracy. To further improve the generalization performance of the proposed model, the Gauss kernel is introduced, thus the non-linear classification problem can be linearly separated in higher dimensional feature space. Two UCI credit datasets and a real life credit dataset from a US major commercial bank are used to check the efficiency of this model. Compared with other popular methods, satisfactory results are obtained through a novel method in the area of credit risk evaluation. So the new model is an excellent choice.

APPLICATION OF PENALTY FUNCTION METHOD IN ISOPARANIETRIC HYBRID FINITE ELEMENT ANALYSIS

By the aid of the penalty function method, the equilibrium restriction conditions were introduced to the isoparametric hybrid finite element analysis, and the concrete application course of the penalty function method in three-dimensional isoparametdc hybrid finite element was discussed. The separated penalty parameters method and the optimal hybrid element model with penalty balance were also presented. The penalty balance method can effectively refrain the parasitical stress on the premise of no additional degrees of freedom. The numeric experiment shows that the presented element not only is effective in improving greatly the numeric calculation precision of distorted grids but also has the universality.

Pathogen Isolation and Identification of a Serious Leaf Disease of Red Sandalwood (Pterocarpus santalinus) Seedlings

In the introduction and propagation of red sandalwood （Pterocarpus santalinus）, a serious leaf disease of its seedlings in winter and spring seasons was found, but the name of the disease and its pathogen species have not been reported. The pathogen isolated from infected leaves of 18-month-old seedlings was identi- fied as Colletotrichum gloeosporioides by morphological characteristics of colony and conidium, and analysis results of rDNA-intemal transcribed spacer sequence （ITS） of the strain. Pathogenicity test results further confirmed that C. gloeosporioides was the pathogen responsible for the infected leaves symptoms of red sandal- wood. However, the disease belongs to an atypical anthraenose. Control of the leaf diseases of red sandalwood seedlings was discussed.

Integral Global Minimization of Constrained Problems with Discontinuous Penalty Functions

A class of discontinuous penalty functions was proposed to solve constrained minimization problems with the integral approach to global optimization, m-mean value and v-variance optimality conditions of a constrained and penalized minimization problem were investigated. A nonsequential algorithm was proposed. Numerical examples were given to illustrate the effectiveness of the algorithm.

Optimal congestion control algorithm for ad hoc networks: Penalty function-based approach

In this paper, based on the inherent characteristic of the contention relation between flows in ad hoc networks, we introduce the notion of the link＇s interference set, extend the utility maximization problem representing congestion control in wireline networks to ad hoc networks, apply the penalty function approach and the subgradient method to solve this problem, and propose the congestion control algorithm Penalty function-based Optical Congestion Control （POCC） which is implemented in NS2- simulator. Specifically, each link transmits periodically the information on its congestion state to its interference set; the set ; the sermon at each source adjusts the transmission rate based on the optimal tradeoffbetween the utility value and the congestion level which the interference set of the links that this session goes though suffers from. MATLAB-based simulation results showed that POCC can approach the globally optimal solution. The NS2-based simulation results showed that POCC outperforms default TCP and ATCP to achieve efficient and fair resource allocation in ad hoc networks.

A Smoothing Penalty Function Method for the Constrained Optimization Problem

In this paper, an approximate smoothing approach to the non-differentiable exact penalty function is proposed for the constrained optimization problem. A simple smoothed penalty algorithm is given, and its convergence is discussed. A practical algorithm to compute approximate optimal solution is given as well as computational experiments to demonstrate its efficiency.