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This paper addresses the long-standing question of whether parental separation shapes offspring school performance drawing on retrospective data from a group of university students in Italy, a country that has historically had very low separation rates yet has experienced an important rise in family dissolution over the last two decades. Using survey data from 1st-year enrollees in undergraduate courses in Economics and Statistics at two points in time (2000 and 2017), we explore whether the association between parental separation and children's school performance (i) is observed both at the level of middle school (MS) and high school (HS); (ii) varies according to the age at which students experienced separation, and (iii) varies by parental socio-economic status (SES). Our results suggest that students whose parents separated before MS completion score on average 2 points less on the end-of-middle-school exam relative to students whose parents did not separate or separated after MS completion. Similarly, students whose parents separated before HS completion score 1 point less on the end-of-high-school exam relative to their counterpart. As such, we document a parental separation penalty both at the level of MS and HS, yet lower by half when separation occurs later in students' life course. Moreover, for HS grade parental separation is associated with more negative consequences when students experience it at very young ages (0–5), while for MS grade heterogeneity by age is less clear-cut. Such penalties are concentrated among children of low-educated mothers, particularly for HS, providing support to the "compensatory advantage" hypothesis predicting smaller penalties for children of high-SES parents. Overall, findings from this study corroborate existing evidence suggesting that the consequences of parental separation vary depending on a combination of social background and the "selectivity" of the educational outcome considered. [ABSTRACT FROM AUTHOR]

Dropout is perhaps the most popular regularization method for deep learning. Due to the stochastic nature of the Dropout mechanism, the convergence analysis of Dropout learning is challenging and the existing convergence results are mainly of probability nature. In this paper, we investigate the deterministic convergence of the mini-batch gradient learning method with Dropconnect and penalty. By drawing and presenting a set of samples of the mask matrix of Dropconnect regularization into the learning process in a cyclic manner, we establish an upper bound of the norm of the weight vector sequence and prove that the gradient of the cost function, the cost function itself, and the weight vector sequence deterministically converge to zero, a constant, and a fixed point respectively. Considering Dropout is mathematically a specific realization of Dropconnect, the established theoretical results in this paper are also valid for Dropout learning. Illustrative simulations on the MNIST dataset are provided to verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper aims to decipher the elliptic PDEs defined over curved complex domains with a penalty approach in a finite difference scheme. Using the penalty approach, we utilize the finite difference scheme with the aid of uniform Cartesian mesh irrespective of the shape and convexity of the domain. We introduce the finite difference schemes with the H 1 and L 2 penalties and furnish the sharp convergence of the solution of the penalized problems to the solution of the original problem. Several iterative solvers are also equipped for both the H 1 and L 2 penalty schemes to compare the required number of iterations and CPU time to achieve the desired accuracy. The comprehensive numerical experiments are performed to uphold the mathematical results and to exhibit the efficiency of the proposed idea for practical scenarios. [ABSTRACT FROM AUTHOR]

Spherical k-means clustering as a known NP-hard variant of the k-means problem has broad applications in data mining. In contrast to k-means, it aims to partition a collection of given data distributed on a spherical surface into k sets so as to minimize the within-cluster sum of cosine dissimilarity. In the paper, we introduce spherical k-means clustering with penalties and give a 2 max { 2 , M } (1 + M) (ln k + 2) -approximation algorithm. Moreover, we prove that when against spherical k-means clustering with penalties but on separable instances, our algorithm is with an approximation ratio 2 max { 3 , M + 1 } with high probability, where M is the ratio of the maximal and the minimal penalty cost of the given data set. [ABSTRACT FROM AUTHOR]

We propose, analyze and test a new adaptive penalty scheme that picks the penalty parameter ϵ element by element small where ∇ · u h is large. We start by analyzing and testing the new scheme on the most simple but interesting setting, the Stokes problem. Finally, we extend and test the algorithm on the incompressible Navier Stokes equation on complex flow problems. Tests indicate that the new adaptive- ϵ penalty method algorithm predicts flow behavior accurately. The scheme is developed in the penalty method but also can be used to pick a grad-div stabilization parameter. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) generation plants, due to the intermittent nature of their output power, can benefit from the integration of Battery Energy Storage Systems (BESSs). In this context, this work proposes an optimized energy management system (EMS) for a joint operation of BESS in utility-scale PV plants (PV/BESS) aiming to profit maximization. The optimization of the BESS operation was achieved from the genetic algorithm, and for the problem formulation, the following cost functions were defined in the objective function: the energy pricing system; the costs associated with degradation and total loss of the batteries; the penalty related to exceeding the maximum power contracted. The proposed EMS defines the reference for a BESS to be integrated into a PV plant located in Coremas, Brazil. The results were obtained from a simulation architecture similar to the PV plant and demonstrated positive financial gains, compared to the operation of the PV plant without BESS. In the simulation of a whole year of operation, it was possible to achieve an additional daily revenue gain of up to 11%. [ABSTRACT FROM AUTHOR]

Assuming a favorable or an adverse outcome for every combination of a credit class and an industry sector, a binary string, termed as a macroeconomic scenario, is considered. Given historical transition counts and a model for dependence among credit-rating migrations, a probability is assigned to each of the scenarios by maximizing a likelihood function. Applications of this distribution in financial risk analysis are suggested. Two classifications are considered: 7 non-default credit classes with 6 industry sectors and 2 non-default credit classes with 12 industry sectors. We propose a heuristic algorithm for solving the corresponding maximization problems of combinatorial complexity. Probabilities and correlations characterizing riskiness of random events involving several industry sectors and credit classes are reported. [ABSTRACT FROM AUTHOR]

In this paper, a semi-parametric regression model with an adaptive LASSO penalty imposed on both the linear and the nonlinear components of the mode is considered. The model is rewritten so that a signed-rank technique can be used for estimation. The nonlinear part consists of a covariate that enters the model nonlinearly via an unknown function that is estimated using B-splines. The author shows that the resulting estimator is consistent under heavy-tailed distributions and asymptotic normality results are given. Monte Carlo simulations as well as practical applications are studied to assess the validity of the proposed estimation method. [ABSTRACT FROM AUTHOR]

This article reviews recent case law of the European Court of Human Rights which has been concerned with the extent to which national decision-making has achieved a fair balance between two rights under the European Convention on Human Rights, namely, the right to reputation and the right to freedom of expression guaranteed respectively by Articles 8 and 10. This case law reveals a consistent approach by the European Court of Human Rights, providing good guidance as to how to resolve the two competing rights. [ABSTRACT FROM AUTHOR]

The hitting set problem is a generalization of the vertex cover problem to hypergraphs. Xu et al. (Theor Comput Sci 630:117–125, 2016) presented a primal-dual algorithm for the submodular vertex cover problem with linear/submodular penalties. Motivated by their work, we study the submodular hitting set problem with linear penalties (SHSLP). The goal of the SHSLP is to select a vertex subset in the hypergraph to cover some hyperedges and penalize the uncovered ones such that the total cost of covering and penalty is minimized. Based on the primal-dual scheme, we obtain a k-approximation algorithm for the SHSLP, where k is the maximum number of vertices in all hyperedges. [ABSTRACT FROM AUTHOR]

Antony Duff's The Realm of Criminal Law offers an appealing moral reconstruction of the criminal law. I agree that the criminal law should be understood to predicate punishment upon sufficient proof that the defendant has committed a public wrong for which she is being held to account and censured. But the criminal law is not only about censoring people for public wrongs; it must serve other purposes as well, such as preventing people from committing serious crimes and more generally from violating reasonable regulations. These purposes, and perhaps retributive justice, require the criminal law also to mete out harsh treatment, but only insofar as such treatments are proportional to the culpable wrong committed. The problem for the criminal law is that many mala prohibita crimes consist of a minor wrong but also call for a relatively severe punishment. To accommodate that mismatch, it is necessary to complement the criminal law, as Duff and I conceive of it, with what I call "penal law." Penal law relies on forfeiture to explain why hard treatment is permissible. The forfeiture must be fair, and it comes with its own proportionality limits. But those limits are not as strict as the limits implicit in the criminal law. It allows for penalties that are harsher than the punishments that could justifiably be meted out for many mala prohibita offenses. One and the same act can count as a crime and a penal infraction, and one and the same criminal justice system can and should handle both crimes and the penal infractions. It is, I think, only in that way that we can accommodate both the need to prevent public wrongdoing and the distinct importance of holding people accountable for the commission of public wrongs. [ABSTRACT FROM AUTHOR]

Experts and popular belief contend that it pays to engage in financial misconduct. We hand-collect data on three subsamples of severe misconduct cases, between 2003 and 2016: a sample of 37 (100) SEC enforcement actions (class action lawsuits) that explicitly allege fraud and a sample of 100 restatements with the most negative stock price reaction in which investors presumably suspect fraud. We then compare estimates of the benefits from reporting misconduct to top managers against estimates of the costs of its discovery. We find that 32.9% of perpetrators experience an overall net benefit from discovered misconduct. The percentage of officers who benefit is highest for the restatement subsample (43.5%), followed by the class action lawsuit subsample (27.7%), and is the lowest for the SEC enforcement subsample (8.1%). Stated differently, if we assume that the probability of detection is 31%, as conjectured in the literature, more than half of the perpetrators in our sample would benefit from engaging in financial reporting misconduct. Hence our evidence suggests that financial reporting misconduct can pay off for perpetrators. [ABSTRACT FROM AUTHOR]

We present the results of a series of economic laboratory experiments designed to study the compliance behavior of polluting firms when penalties are stochastic. The experiments consist of a regulatory environment in which university students faced emission standards and an enforcement mechanism composed of audit probabilities and penalties (conditional on detection of a violation). We examine how uncertainty about the penalty affects the compliance decision and the extent of violation with two levels of enforcement: one in which the regulator induces perfect compliance and another one in which it does not. Our results suggest that in the first case, uncertain penalties increase the extent of violations in firms with higher marginal benefits. When enforcement is not sufficient to induce compliance, the uncertain penalties do not have any statistically significant effect on compliance behavior. Overall, the results suggest that a cost-effective design of emission standards should include complete, public information on the penalties for violations. [ABSTRACT FROM AUTHOR]

Based on a novel algorithm, known as the upper-layer-solution-aware (USA), a new algorithm, in which the penalty method is introduced into the empirical risk, is studied for training feed-forward neural networks in this paper, named as USA with penalty. Both theoretical analysis and numerical results show that it can control the magnitude of weights of the networks. Moreover, the deterministic theoretical analysis of the new algorithm is proved. The monotonicity of the empirical risk with penalty term is guaranteed in the training procedure. The weak and strong convergence results indicate that the gradient of the total error function with respect to weights tends to zero, and the weight sequence goes to a fixed point when the iterations approach positive infinity. Numerical experiment has been implemented and effectively verifies the proved theoretical results. [ABSTRACT FROM AUTHOR]

To meet the demand of various high-speed data rate services as well as serving an exponential increment of mobile devices, millimeter-Wave (mmWave) communication in ultra-dense networks (UDNs) has been considered as a promising technology for future wireless communication systems. By deploying multiple small-cell base stations (SBSs), the dense topology combining high frequency mmWave is expected to grow not only the users (UEs) throughput but also the energy efficiency (EE) of the whole networks. Exploiting the benefits from mmWave and UDNs, in this paper, we introduce a new approach for jointly optimizing SBS-UE association and power allocation to maximize the system EE while guaranteeing the quality of service (QoS) constraints for each UE. Specifically, the throughput fairness among UEs is also taken into account by formulating UE throughput maxmin problem. Unfortunately, such SBS-UE association problem poses a new challenge since it reflects as a complex mixed-integer non-convex problem. On the other hand, the power allocation problem is in nonconvexity structure, which is impossible to handle with the association problem concurrently. Tackling those issues, an alternating descent method is proposed to separate the primal optimization problem into two subproblems and handle one-by-one at each iteration. In particular, the SBS-UE association problem is reformulated using the penalty approach. Then, successive convex programming is developed to convert non-convex problem into the simple convex quadratic functions at each iteration. Numerical results are provided to demonstrate the convergence and low-complexity of our proposed schemes, where the increment of the objective function is guaranteed at each iteration. [ABSTRACT FROM AUTHOR]

The k-means problem is a classic NP-hard problem in machine learning and computational geometry. And its goal is to separate the given set into k clusters according to the minimal squared distance. The k-means problem with penalties, as one generalization of k-means problem, allows that some point need not be clustered instead of being paid some penalty. In this paper, we study the k-means problem with penalties by using the seeding algorithm. We propose that the accuracy only involves the ratio of the maximal penalty value to the minimal one. When the penalty is uniform, the approximation factor reduces to the same one for the k-means problem. Moreover, our result generalizes the k-means++ for k-means problem to the penalty version. Numerical experiments show that our seeding algorithm is more effective than the one without using seeding. [ABSTRACT FROM AUTHOR]

An important significance of restructuring in power industry is the emergence of regulatory performance indices in the power distribution companies. Reward-penalty scheme is a direct control method that regulators use to assure the performance regulatory controls such as system reliability, service efficiency, and customer interruption cost. It is used as a financial tool designed to prevent the service reliability and efficiency deterioration of power distribution companies. Reward penalty scheme rewards the distribution company which provides good reliable power and penalizes those who act otherwise. In this paper, we propose a Cubic Reward-Penalty scheme, to improve the service reliability of power distribution companies. The novel feature of this scheme is the use of cubic reward/penalty formulation which varies in a cubic fashion based on the behavior of the performance-based reliability. In this paper, two methods of implementing Cubic Reward-Penalty scheme are explained: CRPS with no dead band and no reward/penalty capping and CRPS with dead band and reward/penalty capping. These proposed approaches are mainly intended for the regulators who are concerned about the customers affected by the outage duration. The significance of this research, lies in encouraging power distribution companies, to maintain, or even improve, customer service and satisfaction, by developing the Performance Regulatory Reward-Penalty schemes. This paper also provides most of the requirements and design rationale of Cubic Reward-Penalty Structure, its theoretical foundation and its prototype implementation. [ABSTRACT FROM AUTHOR]

In this paper, we study the k-means problem with (nonuniform) penalties (k-MPWP) which is a natural generalization of the classic k-means problem. In the k-MPWP, we are given an n-client set D⊂Rd, a penalty cost pj>0 for each j∈D, and an integer k≤n. The goal is to open a center subset F⊂Rd with |F|≤k and to choose a client subset P⊆D as the penalized client set such that the total cost (including the sum of squares of distance for each client in D\P to the nearest open center and the sum of penalty cost for each client in P) is minimized. We offer a local search (81+ε)-approximation algorithm for the k-MPWP by using single-swap operation. We further improve the above approximation ratio to (25+ε) by using multi-swap operation. [ABSTRACT FROM AUTHOR]

In this article the error analysis in the paper, stabilized Lagrange multiplier method for elliptic and parabolic interface problems are extended to the case of natural norm which is independent of mesh size for the case of elliptic interface problems. A stabilized Lagrange multiplier method for second order elliptic interface problems is presented in the framework of mortar method. The requirement of Ladyzhenskaya–Babuška–Brezzi condition for mortar method is alleviated by introducing penalty terms in the formulation. Optimal convergence results are established. Numerical experiments are conducted in support of the theoretical derivations. [ABSTRACT FROM AUTHOR]

Cloud Computing is a gathering of physical and virtualized assets gave to the clients according to request and pay per uses bases via internet. Basically, the task scheduling and resource allocation two features are considered such as cost and makespan. In order to achieve better performance in task scheduling, resource allocation and task scheduling must be precisely organized and optimized jointly. Several works have been published in the literature to do the scheduling in cloud. In this paper, for enhancing the scheduling process cuckoo search (CS) and harmony search (HS) algorithm is hybrid as CHSA to improve the optimization problem. These two algorithms are effectively combined to do intelligent process scheduling. According to this, a new multi-objective function is proposed by combining cost, energy consumption, memory usage, credit and penalty. Finally, the performance of the CHSA algorithm is compared with different algorithms such as existing hybrid cuckoo gravitational search algorithm, individual CS and HS algorithm with various multi-objective parameters. By analyzing the result our proposed CHSA algorithm attain minimum cost, minimum memory usage, minimum energy consumption, minimum penalty and maximum credit compared to existing techniques. [ABSTRACT FROM AUTHOR]

As specified from the marketing standpoint, firm hydro-electrical energy must be available on an assured basis. To achieve the firm energy yield at maximum level, simultaneous optimization of operational variables is utilized. The variables include reservoir releases over historical stream-inflow records as well as the plant factor. Here, the plant factor denotes the percentage of day for operating a peak-time power plant at full capacity. To this end, a differential evaluation (DE) algorithm equipped with an indirect constraint handling is employed, in which an adaptive penalty system imposes the desirable reliability and preserves the total energy generation. To implement the model for a real example, a reservoir dam named Karun4, in southwest of Iran, is applied. The experimental results show the firm energy produced by the current and developed method are 49.2 and 127.5 (10 watt hours), respectively. Also, the results reveal the maximum capability of generating the firm energy yield with target reliability whereas the total energy generation is preserving. [ABSTRACT FROM AUTHOR]

We present a conservative formulation for the frictional contact forces developed in the framework of an energy-consistent method. Bilateral constrains, that is, joints and rigid links, are imposed using the augmented Lagrange technique, provided that the constraints are exactly satisfied. We propose a formulation based on a penalty method to deal with the contact problem including damping and friction. The numerical scheme guarantees that the energy is consistently preserved or unconditionally dissipated. The treatment of the contact constraint also constitutes a novel aspect of this investigation. It is based on the description of implicit surfaces given an efficient strategy for detecting and evaluating the contact. The numerical tests exhibit a good energy preservation performance. Moreover, we use the proposed formulation to analyze a mechanism including a revolute joint with frictional clearance. [ABSTRACT FROM AUTHOR]

In this paper, a challenge issue 'sensing and data transmission trade-off' is addressed. Different from existing efforts, penalty is taken as a rule of regulation for spectrum sensing and access in order to possibly protect primary's activity. Specifically, secondary user needs to pay rental fee to primary user for opportunistic access. However, penalty will be charged from secondary user as well if it has transmitted on the miss estimated spectrum, in which, an negative secondary raw utility will be generated when the penalty value is properly decided. Thus the scheme will further force secondary user to improve its sensing accuracy and select the best transmission strategy when compared with conventional sensing access model. Based on the scheme, in this research, the bounds for proper rental and penalty prices are derived. Further, within both proper prices region, an optimization problem for setting sensing duration and transmission power is formulated. Through investigation, convexity of the problem is hard to be obtained at global view. But thanks to the special property that the problem is local convex when fixing any one of targeted variables, a Lagrange method based iterative algorithm is presented to find solution within a decent convergence of no more than 10 iterations. [ABSTRACT FROM AUTHOR]

Composite indices for measuring multidimensional phenomena have become very popular in a variety of economic, social and policy domains. The literature offers a wide range of aggregation methods, all with their pros and cons. The most used are additive methods, but they imply requirements and properties which are often not desirable or difficult to meet. For example, they assume a full substitutability among the components of the index: a deficit in one dimension can be compensated by a surplus in another. In this paper, we consider a non-compensatory composite index for spatial comparisons and its variant for spatio-temporal comparisons. A study of the aggregation function is, for the first time, presented and its main properties are formally defined. As an example of application, a set of individual indicators of well-being for OECD countries is considered and a comparison between the two approaches is provided, in order to show what they offer and how they work. [ABSTRACT FROM AUTHOR]

This paper considers a special class of convex programming (CP) problems whose feasible regions consist of a simple compact convex set intersected with an affine manifold. We present first-order methods for this class of problems based on an inexact version of the classical augmented Lagrangian (AL) approach, where the subproblems are approximately solved by means of Nesterov's optimal method. We then establish a bound on the total number of Nesterov's optimal iterations, i.e., the inner iterations, performed throughout the entire inexact AL method to obtain a near primal-dual optimal solution. We also present variants with possibly better iteration-complexity bounds than the original inexact AL method, which consist of applying the original approach directly to a perturbed problem obtained by adding a strongly convex component to the objective function of the CP problem. [ABSTRACT FROM AUTHOR]

The aim of this work is to propose a formulation to solve both small and large deformation contact problems using the finite element method. We consider both standard finite elements and the so-called immersed boundary elements. The method is derived from a stabilized Nitsche formulation. After introduction of a suitable Lagrange multiplier discretization the method can be simplified to obtain a modified perturbed Lagrangian formulation. The stabilizing term is iteratively computed using a smooth stress field. The method is simple to implement and the numerical results show that it is robust. The optimal convergence rate of the finite element solution can be achieved for linear elements. [ABSTRACT FROM AUTHOR]

The paper's central focus is the 'duty' theory of punishment developed by Victor Tadros in The Ends of Harm. In evaluating the 'duty' theory we might ask two broad closely related questions: whether in its own terms the 'duty' theory provides a justification of the imposition of hard treatment or suffering on an offender; and whether the 'duty' theory can provide a justification of punishment. This paper is principally concerned with the second question, which stems from a significant difference between Tadros's 'duty view' of punishment, as opposed to more familiar theories that seek to justify punishment as essentially the imposition of a penalty for (purported) wrongdoing. In addressing this question I highlight this particular difference as problematic for Tadros's 'duty' theory. The issues concern Tadros's conception of punishment and the central features of his 'duty view': the claim that punishment of some offenders can be justified as the (enforced) fulfilment of a duty of protection that they owe principally to those whom they have wrongfully harmed. [ABSTRACT FROM AUTHOR]

Penalty is a crucial approach to maintain society in order in both the legal and the political philosophies. How to establish a rational and efficient penalty mechanism is exceedingly important in practice in economics and politics and this paper explores the optimal mechanism design of penalty. A penalty under monopoly mechanism design theory is established and developed in this piece of work. By establishing the penalty mechanism design model, this paper finds that stricter punishment can efficiently deter violation of the regulations but can decrease the profits of the monopoly firm at the same time. Furthermore, penalty increases the concavity of the monopoly firm's profit function, which makes it easier for the firm to make decisions and which means strict penalty results in optimal decisions. We also show that punishment is in general costly, which is highly consistent with the phenomena in practice. [ABSTRACT FROM AUTHOR]

This paper investigates the split-complex back-propagation algorithm with momentum and penalty for training complex-valued neural networks. Here the momentum are used to accelerate the convergence of the algorithm and the penalty are used to control the magnitude of the network weights. The sufficient conditions for the learning rate, the momentum factor, the penalty coefficient, and the activation functions are proposed to establish the theoretical results of the algorithm. We theoretically prove the boundedness of the network weights during the training process, which is usually used as a precondition for convergence analysis in literatures. The monotonicity of the error function and the convergence of the algorithm are also guaranteed. [ABSTRACT FROM AUTHOR]

Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates. The presented approach to the fitting of generalized linear mixed models includes an L-penalty term that enforces variable selection and shrinkage simultaneously. A gradient ascent algorithm is proposed that allows to maximize the penalized log-likelihood yielding models with reduced complexity. In contrast to common procedures it can be used in high-dimensional settings where a large number of potentially influential explanatory variables is available. The method is investigated in simulation studies and illustrated by use of real data sets. [ABSTRACT FROM AUTHOR]

We report results from economic experiments of decisions that are best described as petty larceny, with high school and college students who can anonymously steal real money from each other. Our design allows exogenous variation in the rewards of crime, and the penalty and probability of detection. We find that the probability of stealing is increasing in the amount of money that can be stolen, and that it is decreasing in the probability of getting caught and in the penalty for getting caught. Furthermore, the impact of the certainty of getting caught is larger when the penalty is bigger, and the impact of the penalty is bigger when the probability of getting caught is larger. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 10 $ was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes. [ABSTRACT FROM AUTHOR]

In this paper, we examine the potential determinants of foreign direct investment. For this purpose, we apply new exact subset selection procedures, which are based on idealized assumptions, as well as their possibly more plausible empirical counterparts to an international data set to select the optimal set of predictors. Unlike the standard model selection procedures AIC and BIC, which penalize only the number of variables included in a model, and the subset selection procedures RIC and MRIC, which consider also the total number of available candidate variables, our data-specific procedures even take the correlation structure of all candidate variables into account. Our main focus is on a new procedure, which we have designed for situations where some of the potential predictors are certain to be included in the model. For a sample of 73 developing countries, this procedure selects only four variables, namely imports, net income from abroad, gross capital formation, and GDP per capita. An important secondary finding of our study is that the data-specific procedures, which are based on extensive simulations and are therefore very time-consuming, can be approximated reasonably well by the much simpler exact methods. [ABSTRACT FROM AUTHOR]

In this paper, the problem of allocation of the profit, obtained from a fuzzy coalition, among its players is considered. It is argued that this allocation is influenced by satisfaction of the players in regards to better performance and success within a cooperative endeavour. Our model is based on the real life situations, where possibly one or more players compromise on their payoffs in order to help forming a coalition. We have developed a dynamic approach to obtain a suitable solution to the corresponding cooperative fuzzy game. Further, the notion of a penalty among the bargaining players is introduced. This would inflict them to reasonable demands only. [ABSTRACT FROM AUTHOR]

The basic requirement for the stability of the mortar element method is to construct finite element spaces which satisfy certain criteria known as inf-sup (well known as LBB, i.e., Ladyzhenskaya-Babuška-Brezzi) condition. Many natural and convenient choices of finite element spaces are ruled out as these spaces may not satisfy the inf-sup condition. In order to alleviate this problem Lagrange multiplier method with penalty is used in this paper. The existence and uniqueness results of the discrete problem are discussed without using the discrete LBB condition. We have also analyzed the Lagrange multiplier method with penalty for parabolic initial-boundary value problems using semidiscrete and fully discrete schemes. We have derived sub-optimal order of estimates for both semidiscrete and fully discrete schemes. The results of numerical experiments support the theoretical results obtained in this article. [ABSTRACT FROM AUTHOR]

modeling method aimed at eliminating the need of explicit crack representation in bi-dimensional structures is presented for the simulation of the initiation and subsequent propagation within composite materials. This is achieved by combining a meshless method with a physical stress-displacement based criterion known as Cohesive Model. This model consents to apply a penalty-based approach to delamination modeling where a variable penalty factor along the crack segment allows to loosen or tight the two parts according to their relative displacements. Results are showed for classical single mode loading benchmark cases and compared to experimental results taken from the literature. [ABSTRACT FROM AUTHOR]

This paper applies bilinear immersed finite elements (IFEs) in the interior penalty discontinuous Galerkin (DG) methods for solving a second order elliptic equation with discontinuous coefficient. A discontinuous bilinear IFE space is constructed and applied to both the symmetric and nonsymmetric interior penalty DG formulations. The new methods can solve an interface problem on a Cartesian mesh independent of the interface with local refinement at any locations needed even if the interface has a nontrivial geometry. Numerical examples are provided to show features of these methods. [ABSTRACT FROM AUTHOR]

This paper studies the problem of estimating the number of clusters in the context of logistic regression clustering. The classification likelihood approach is employed to tackle this problem. A model-selection based criterion for selecting the number of logistic curves is proposed and its asymptotic property is also considered. The small sample performance of the proposed criterion is studied by Monto Carlo simulation. In addition, a real data example is presented. [ABSTRACT FROM AUTHOR]

The ensemble Kalman Filter (EnKF) applied to a simple fire propagation model by a nonlinear convection-diffusion-reaction partial differential equation breaks down because the EnKF creates nonphysical ensemble members with large gradients. A modification of the EnKF is proposed by adding a regularization term that penalizes large gradients. The method is implemented by applying the EnKF formulas twice, with the regularization term as another observation. The regularization step is also interpreted as a shrinkage of the prior distribution. Numerical results are given to illustrate success of the new method. [ABSTRACT FROM AUTHOR]

Multidimensional unfolding methods suffer from the degeneracy problem in almost all circumstances. Most degeneracies are easily recognized: the solutions are perfect but trivial, characterized by approximately equal distances between points from different sets. A definition of an absolutely degenerate solution is proposed, which makes clear that these solutions only occur when an intercept is present in the transformation function. Many solutions for the degeneracy problem have been proposed and tested, but with little success so far. In this paper, we offer a substantial modification of an approach initiated by Kruskal and Carroll (1969) that introduced a normalization factor based on the variance in the usual least squares loss function. Heiser (unpublished thesis, 1981) and De Leeuw (1983) showed that the normalization factor proposed by Kruskal and Carroll was not strong enough to avoid degeneracies. The factor proposed in the present paper, based on the coefficient of variation, discourages or penalizes (nonmetric) transformations of the proximities with small variation, so that the procedure steers away from solutions with small variation in the interpoint distances. An algorithm is described for minimizing the re-adjusted loss function, based on iterative majorization. The results of a simulation study are discussed, in which the optimal range of the penalty parameters is determined. Two empirical data sets are analyzed by our method, clearly showing the benefits of the proposed loss function. [ABSTRACT FROM AUTHOR]

In this work, a new penalty formulation is proposed for the analysis of Mindlin-Reissner plates by using the element-free Galerkin method. A penalized weak form for the Mindlin-Reissner Plates is constructed through the exterior penalty method to enforce the essential boundary conditions of rotations as well as transverse displacements. In the numerical examples, some typical problems of Mindlin-Reissner plates are analyzed, and parametric studies on the order of integration and the size of influence domain are also carried out. The effect of the types of background cells on the accuracy of numerical solutions is observed and a proper type of background cell for obtaining optimal accuracy is suggested. Further, optimal order of integration and basis order of Moving Least Squares approximation are suggested to efficiently handle the irregularly distributed nodes through the triangular type of background cells. From the numerical tests, it is identified that unlike the finite element method, the proposed element-free Galerkin method with penalty technique gives highly accurate solution without shear locking in dealing with Mindlin-Reissner plates. [ABSTRACT FROM AUTHOR]

An implicit algorithm of Bi-penalty approximation with orthogonality projection for the numerical simulation of Bingham fluid flow problems is proposed in this paper. A Newton fluid flow with two kinds of artificial viscosity subjected to the inequality constraint is introduced to approximate the Bingham fluid flow. This approach can effectively simulate the Bingham fluid flow with floating rigid cores or fixing rigid cores. [ABSTRACT FROM AUTHOR]

We discuss several possible legal principles from the standpoint of Bayesian decision theory. In particular, we show that a compelling legal principle implies compatibility with decisions based on maximizing the expected utility. [ABSTRACT FROM AUTHOR]