Penalty Approach Research Articles

Iran's Criminal Policy on Economic Crimes

This article examines Iran's criminal policy regarding economic crimes. The present research was conducted using a descriptive and analytical method. The expansion of economic crimes has hindered the government’s achievement of national objectives in the domain of economic security. Iran’s economic environment, due to numerous vulnerabilities, creates a more conducive ground for committing economic crimes compared to advanced economic systems. However, Iran’s criminal policy, especially its penal policy, lacks specific measures to address this category of crimes. Due to the absence of a structured penal policy, the generalization of economic crime instances, insufficient coverage of economic crimes, and the lack of specific criteria to distinguish these crimes from other offenses, this concept benefits actual economic criminals more than it does the legal system and society’s citizens. Findings indicate that Iran's criminal policy regarding economic crimes faces numerous challenges. Among the most fundamental challenges and ambiguities are the lack of a comprehensive and precise definition of the concept and purpose of economic crimes, the breadth of cases due to leniency in judicial approaches, the flawed adoption of penal policies and the degree of their appropriateness and effectiveness, the absence of a clear definition of economic crimes, multiple legislative bodies in the field of economic crimes, pluralism in judicial criminal policy, populist penal approaches in dealing with economic crimes, the absence of a cohesive and unified criminal policy, and the security-oriented nature of criminal policy in economic crimes. These are among the current challenges and issues in Iran's criminal policy regarding economic crimes.

Sparse identification modeling and predictive control of wafer temperature in an atomic layer etching reactor

To address the escalating demand and supply constraints for semiconductor devices, manufacturing techniques such as thermal atomic layer etching (ALE) have been utilized, which require robust temperature control systems. Radiative heating with lamps is a promising method for achieving fast and precise temperature control. However, there is limited research in analyzing the dynamic control of radiative lamp heating systems. In this study, a transient, two-dimensional (2-D) radiative heating model in a cross-flow thermal ALE reactor is constructed through Ansys Fluent. A sparse identification (SINDy) modeling approach is applied to build a reduced-order dynamic model for the prediction of system states in the control system. A model predictive controller (MPC) is then developed to bring the wafer surface temperature to the target temperature while preserving the uniformity of the temperature on the wafer surface. Control is implemented by adjusting the powers of three groups of heating lamps independently. Notably, a novel feedback-based time-varying steady-state penalty approach is applied with the MPC in this study, which enables the system to reach the target temperature range within 10 s while maintaining temperature uniformity for 1000 s.

Broadband shock vibration absorber based on vibro-impacts and acoustic black hole effect

A vibration absorber combining the acoustical black hole (ABH) effect with vibro-impacts is presented as a separate device that can be mounted on a primary structure for passive vibration mitigation. The vibration isolator is shown to be effective for low-frequency shock vibration attenuation. Numerical and experimental results are reported, considering a rectangular honeycomb plate as the primary structure, on which the device, consisting of a circular ABH plate with one or several impact masses, is attached. A numerical model is developed using a finite element space discretization combined with a conservative scheme in a penalty approach for the contact dynamics. The analysis shows that the device has broadband efficiency thanks to the combined effects of energy transfer due to impacts and high attenuation in mid and high-frequency range due to the ABH effect. A detailed parametric study underlines that the tuning of the linear fundamental frequency of the impact mass plays an important role. Besides, considering several impact masses is shown to improve the efficacy. An experimental setup is then used in order to demonstrate the effectiveness of the device. The results confirm the vibration mitigation trends obtained from the numerical model that could then be used as a design tool for the attenuator. Finally, an analysis based on the Shock Response Spectrum (SRS), widely used in the field of aerospace engineering, is performed, underlining the attenuator is able to reduce rapidly and efficiently the vibrations due to a mechanical shock.

A collective neurodynamic penalty approach to nonconvex distributed constrained optimization

A nonconvex distributed optimization problem involving nonconvex objective functions and inequality constraints within an undirected multi-agent network is considered. Each agent communicates with its neighbors while only obtaining its individual local information (i.e. its constraint and objective function information). To overcome the challenge caused by the nonconvexity of the objective function, a collective neurodynamic penalty approach in the framework of particle swarm optimization is proposed. The state solution convergence of every neurodynamic penalty approach is directed towards the critical point ensemble of the nonconvex distributed optimization problem. Furthermore, employing their individual neurodynamic models, each neural network conducts accurate local searches within constraints. Through the utilization of both locally best-known solution information and globally best-known solution information, along with the incremental enhancement of solution quality through iterations, the globally optimal solution for a nonconvex distributed optimization problem can be found. Simulations and an application are presented to demonstrate the effectiveness and feasibility.

Bisection Constraint Method for Multiple-Loading Conditions in Structural Topology Optimization

Topology optimization (TO) is currently a focal point for researchers in the field of structural optimization, with most studies concentrating on single-loading conditions. However, real engineering structures often have to work under various loading conditions. Approaches addressing multiple-loading conditions often necessitate subjective input in order to determine the importance of each loading condition, aiming for a compromise between them. This paper proposes a so-called bisection constraint method (BCM), offering a unique, user-preference-independent solution for TO problems amidst multiple-loading conditions. It is well-known that minimizing the system’s compliance is commonly used in TO as the objective. Generally, compliance is not as sufficient as stress to be used as a response to evaluate the performance of structures. However, formulations focusing on minimizing stress levels usually pose significant difficulties and instabilities. On the other hand, the compliance approach is generally simpler and more capable of providing relatively sturdy designs. Hence, the formulation of min–max compliance is used as the target problem formulation of the proposed method. This method attempts to minimize compliance under only one loading condition while compliances under the remaining loading conditions are constrained. During the optimization process, the optimization problem is automatically reformulated with a new objective function and a new set of constraint functions. The role of compliance under different loading conditions, i.e., whether it is to be treated as an objective or constraint function, might be changed throughout the optimization process until convergence. Several examples based on the solid isotropic material with penalization (SIMP) approach were conducted to illustrate the validity of the proposed method. Furthermore, the general effectiveness of the compliance approach in terms of stress levels is also discussed. The calculation results demonstrated that while the compliance approach is effective in several cases, it proves ineffective in certain scenarios.

A Hessian-based zeroing neurodynamic approach for quaternion-variable time-varying constrained optimization problems

In this paper, a quaternion-variable zeroing neurodynamic (ZND) is designed, in order to address a class of quaternion-variable time-varying convex optimization problems (TCOP) with equality constraints and general inequality constraints. Through utilizing the Karush–Kuhn–Tucker (KKT) condition, the TCOP is transformed into a time-varying equation system for solving. During this process, the introduction of two approaches distinguishes our works in solving TCOPs. On the one hand, the penalty approach is utilized to reduce the influence of time-varying inequality constraints, and thus, makes the ZND more suitable for TCOPs with general and complex inequality constraints. On the other hand, the approach to simplify the KKT condition significantly simplifies the structure of the proposed ZND by reducing the neurons’ number. The proposed ZND realizes efficient convergence while keeping the structure as simple as possible, making it easy to apply in engineering tasks. Besides, to improve its efficiency further, a nonlinear activated function is utilized to achieve a faster convergent rate, by which the convergent rate of the ZND is accelerated to finite-time convergence. Apart from that, the ZND here also owes superior robustness. To the best of our knowledge, this is the first time to extend the ZND for solving TCOP to the quaternion domain. Finally, numerical examples and a robot manipulator control experiment further explain the effectiveness of the proposed ZND.

Design of Hybrid-Laminar-Flow-Control Wing and Suction System for Transonic Midrange Aircraft

Hybrid laminar flow control (HLFC) has shown significant promise in the viscous drag reduction of aircraft. However, the use of HLFC for commercial applications requires further simplification. The current study proposes tools for the conceptual design of transonic HLFC wing and suction system. In the first part of the study, airfoil sections for the wing are optimized for minimum total drag using a multi-objective genetic algorithm approach at six spanwise locations. The induced drag of the wing is estimated using a vortex lattice method solver. In the second part of the study, suction system design is performed using ASPeCT, an in-house solver for HLFC system design. A simplified inner structure for the suction system is proposed, which can be integrated easily within the wing structure. A total drag penalty approach is proposed to establish a tradeoff between matching the target suction distribution and the complexity of the suction system. Finally, the additional weight and off-design performance of the suction system are analyzed for a ±0.1 change in the design lift coefficient. A maximum fuel reduction of 7% can be expected with the HLFC system taking into account the additional weight added and power off-take from the engine.

Juridical Analysis of Corruption Crime Asset Confistion As an Effort to Return State Losses Using Non Penal Approach in Indonesia

In its development, corruption in Indonesia has increased from year to year, both in terms of the quality or amount of state financial losses and the quality of how it is carried out in a sophisticated and systematic manner, and has even penetrated across national borders. Therefore, handling corruption, especially in order to maximize the recovery of state losses, requires a comprehensive approach, as well as through international cooperation. This research found the fact that the asset confiscation mechanism using a non-penal approach is more effective and efficient. This argument is based on the fact that because there is no need to confirm the crime of corruption as long as the perpetrator's assets are suspected to be illegal assets of corruption, this shortens the time. Confiscation of property or assets using non-penal means is more efficient when compared to confiscation of property or assets through civil means in accordance with the provisions of the Corruption Eradication Law (PTPK) which is legal in Indonesia. Because if in a criminal case using civil law it is the same as placing the burden on the prosecutor to carry out proof, then it actually increases the number of things that might happen, namely reconvention lawsuits against the prosecutor as the plaintiff. The asset confiscation mechanism using a non-penal approach no longer focuses on chasing perpetrators but rather following the movement of money so that the number of corruption in Indonesia is expected to decrease.

Assessing the reliability of local earthquake tomography for crustal imaging: 30 yr of records in the Western Alps as a case study

SUMMARY Local earthquake tomography (LET) is a popular method for inverting arrival time picks of local-regional earthquakes for P- and S-wave velocities and hypocentre parameters in seismically active regions. This popularity is due to some robust and well-documented open-source codes that are sometimes used as black boxes. The availability of a very complete time-pick database on the Western Alps gives us a chance to thoroughly investigate the influence of the numerous processes and parameters involved when applying LET to the Western Alps or similar targets. From a subset of high-quality manual picks (1989–2014), we compute preliminary P and S velocity models that are used to predict arrival times for later events and allow the selected fusion of picks downloaded from different seismological agencies for a consistent 33-yr database (1989–2021). Different model reconstructions are proposed by selecting different subsets of the arrival time data set. Aside data input into the LET, influence of initial stratified or 3-D velocity models and related initial earthquake locations is investigated together with grid discretization, Laplacian smoothing and damping parameters in the standard penalty approach commonly used by LET codes. These numerical parameters account for the expected limited resolution of seismic waves due to their finite-frequency content. Parameter selection is handled by the user, whereas frequency impact is only implicit in the onset picks. The earthquake distribution allows a reconstruction down to 40-km depth over an area of ∼500 × 500 km2. Robust features such as the high-velocity Ivrea body anomaly, and a deep low-velocity anomaly associated with crustal thickening underneath the mountain belt survive whatever the tomography strategy and parameters. Finally, a comparison with previous LET reconstructions suggests that finite-frequency content be explicitly incorporated through wave equation tomography to improve spatial resolution. This would fully exploit observables collected from seismograms, albeit with a significant increase in computer costs.

Penalisation Methods in Fitting High‐Dimensional Cointegrated Vector Autoregressive Models: A Review

SummaryCointegration has shown useful for modeling non‐stationary data with long‐run equilibrium relationships among variables, with applications in many fields such as econometrics, climate research and biology. However, the analyses of vector autoregressive models are becoming more difficult as data sets of higher dimensions are becoming available, in particular because the number of parameters is quadratic in the number of variables. This leads to lack of statistical robustness, and regularisation methods are paramount for obtaining valid estimates. In the last decade, many papers have appeared suggesting different penalisation approaches to the inference problem. Here, we make a comprehensive review of different penalisation methods adapted to the specific structure of vector cointegrated models suggested in the literature, with relevant references to software packages. The methods are evaluated and compared according to a range of error measures in a simulation study, considering combinations of low and high dimension of the system and small and large sample sizes.

Topology optimization approach for functionally graded metamaterial components based on homogenization of mechanical variables

The micron resolutions that 3D printers are reaching allow for a change of paradigm in the design of components, optimizing not only the topology of the component, but also the microstructure of the material, which can adopt different objectives at different locations in the component, reaching different mechanical properties and different optimal microstructures at those locations. This constitutes a flip in the design objectives to focus on tailored material work, aimed either at local objectives or at component-wide ones, instead of pursuing homogeneous material properties for global objectives. For this new concurrent structure-material design leading to optimized components made of functionally graded (FG) metamaterials, new topology optimization (TO) procedures are needed in which the objective is to obtain component-wise mechanical variables or distributions of these variables, rather than global component objectives as compliance, which is a typical objective of classical TO approaches.In this work we present a new TO approach aimed at FG metamaterial design pursuing homogeneous mechanical variables. We focus on homogenization of deformation level along the component by minimizing its standard deviation across the domain. The proposed formulation can be naturally generalized to anisotropy, nonlinear behavior, and other optimization objectives. Convergence to a suitable optimized solution, with a distribution of material properties resulting in a homogeneously deformed component, is obtained. These FG material properties may be obtained from local metamaterial design. Classical material/void configurations may also be obtained following our procedure, and results for such cases can be compared qualitatively to the Solid Isotropic Material with Penalization (SIMP) approach.

A coupled implicit MPM-FEM approach for brittle fracture and fragmentation

A new approach has been developed that couples the Material Point Method (MPM) with the Finite Element Method (FEM). The introduced coupling method is achieved by utilizing a bond element to connect the material points to the finite elements, and employing the penalty approach to prevent slippage. Moreover, the developed approach deals with the contact between MPM and FEM. The contact is regulated by introducing additional conditions. When these conditions are met, the bond elements become active and prevent penetration. The coupling is monolithic, meaning that one system of equations is built for both MPM and FEM, and solved simultaneously. Additionally, an automated conversion of finite elements to material points is presented, which is controlled by the eigenfracture approach that splits the finite elements into intact and eroded sets. Eroded finite elements are then converted to material points. Numerical examples are reported to express the novelty of the developed approach.

Crash injury severity prediction considering data imbalance: A Wasserstein generative adversarial network with gradient penalty approach

For each road crash event, it is necessary to predict its injury severity. However, predicting crash injury severity with the imbalanced data frequently results in ineffective classifier. Due to the rarity of severe injuries in road traffic crashes, the crash data is extremely imbalanced among injury severity classes, making it challenging to the training of prediction models. To achieve interclass balance, it is possible to generate certain minority class samples using data augmentation techniques. Aiming to address the imbalance issue of crash injury severity data, this study applies a novel deep learning method, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP), to investigate a massive amount of crash data, which can generate synthetic injury severity data linked to traffic crashes to rebalance the dataset. To evaluate the effectiveness of the WGAN-GP model, we systematically compare performances of various commonly-used sampling techniques (random under-sampling, random over-sampling, synthetic minority over-sampling technique and adaptive synthetic sampling) with respect to dataset balance and crash injury severity prediction. After rebalancing the dataset, this study categorizes the crash injury severity using logistic regression, multilayer perceptron, random forest, AdaBoost and XGBoost. The AUC, specificity and sensitivity are employed as evaluation indicators to compare the prediction performances. Results demonstrate that sampling techniques can considerably improve the prediction performance of minority classes in an imbalanced dataset, and the combination of XGBoost and WGAN-GP performs best with an AUC of 0.794 and a sensitivity of 0.698. Finally, the interpretability of the model is improved by the explainable machine learning technique SHAP (SHapley Additive exPlanation), allowing for a deeper understanding of the effects of each variable on crash injury severity. Findings of this study shed light on the prediction of crash injury severity with data imbalance using data-driven approaches.

Credit Risk Assessment of Heavy-Polluting Enterprises: A Wide-ℓp Penalty and Deep Learning Approach

Effective credit risk assessment of heavy-polluting enterprises can achieve a balance between environmental and economic benefits. It requires the consideration of risk indicators for both the carbon information dimension and the compliance dimension. However, as the feature dimensions of the model continue to increase, so does the irrelevant feature or noise. Therefore, we investigate the use of non-integers for regularization from high-dimensional data under the conditions of a large number of irrelevant features. In this paper, a novel Wide-ℓp Penalty and Deep Learning (WPDL) method for credit risk assessment is proposed, which could provide a sparse solution. The Wide-ℓp Penalty component allows feature selection using a linear model with an ℓp Penalty regularization mechanism, where 0 < p ≤ 2. The deep component is a DNN that can generalize indicator features from the credit risk data. The experimental results show that the minimum prediction error occurs at a non-integer ℓp Penalty. Furthermore, the WPDL outperforms other models such as KNN, DT, RF, SVM, MLP, DNN, Gradient Boosting, and Bagging.

Route Choice Set Generation on High-Resolution Networks

This study seeks to find a strategy to capture the most observed trajectories with a minimum number of algorithms. GPS information on 4,538 real trips from 131 travelers in 2008 was collected and analyzed in Minneapolis-St. Paul (the Twin Cities) as part of the I-35W Bridge Collapse study. The high-resolution road network of the Twin Cities includes 108,561 nodes and 277,747 links. Labeling and link penalty approaches are combined to generate alternatives based on either observed or free-flow speed. Overall, with the best 10 labels, on average, 40 unique routes are generated for each origin-destination pair, and around 80% of all observed trips could be captured with an 80% overlap threshold. About 88% of all observed trips have an average deviation within 50 m compared with the best matching result when combining all labels introduced in this study. Freeway-preferred routes cover more observed trips than freeway-avoided routes, and the peak coverage occurs when freeway travel is weighted between 0.8 to 1 of travel on non-freeway links. A random effects panel model is used for predicting the overlap between alternative route and observed trajectory. Multinomial and mixed logit models with a path-size term are applied to model the route selection. These models indicate that alternative routes which are shorter in distance, have faster average free-flow speed, contain a higher freeway percentage, and incur fewer traffic lights, are more likely to have higher overlap with observed trajectories and are more likely to be selected.

Bayesian Regularized SEM: Current Capabilities and Constraints

An important challenge in statistical modeling is to balance how well our model explains the phenomenon under investigation with the parsimony of this explanation. In structural equation modeling (SEM), penalization approaches that add a penalty term to the estimation procedure have been proposed to achieve this balance. An alternative to the classical penalization approach is Bayesian regularized SEM in which the prior distribution serves as the penalty function. Many different shrinkage priors exist, enabling great flexibility in terms of shrinkage behavior. As a result, different types of shrinkage priors have been proposed for use in a wide variety of SEMs. However, the lack of a general framework and the technical details of these shrinkage methods can make it difficult for researchers outside the field of (Bayesian) regularized SEM to understand and apply these methods in their own work. Therefore, the aim of this paper is to provide an overview of Bayesian regularized SEM, with a focus on the types of SEMs in which Bayesian regularization has been applied as well as available software implementations. Through an empirical example, various open-source software packages for (Bayesian) regularized SEM are illustrated and all code is made available online to aid researchers in applying these methods. Finally, reviewing the current capabilities and constraints of Bayesian regularized SEM identifies several directions for future research.

A linear optimization model for high dose rate brachytherapy using a novel distance metric.

Purpose.We propose a linear network-based optimization model (LNBM) for high dose rate brachytherapy (HDR-BT) that uses a novel distance metric to measure the discrepancy between the dose delivered and the prescription. Unlike models in the literature, LNBM takes advantage of the adjacency structure of the patients' voxels by formalizing them into a network.Methods.We apply LNBM to a set of 7 cervical cancer cases treated with HDR-BT. State-of-the-art commercial optimization software solves LNBM to global optimality. The results of LNBM are compared with those of inverse planning by simulated annealing (IPSA) based on tumor coverage, dosimetric indices for the critical organs at risk (OARs), isodose contour plots, and two metrics of homogeneity new to this work (hot-spots volumes and diameters).Results.LNBM produces plans with improved tumor coverage and with improved isodose contour plots and dosimetric indices for OARs that receive highest dose (bladder and rectum in this study) when compared with IPSA. Using new metrics of homogeneity, we also demonstrate that LNBM produces more homogeneous plans on these cases. An analysis of the solutions of LNBM shows that they use a significant part of the voxel network structure, providing evidence that the plans produced are different from those created using traditional penalty approaches and are more directly guided by the geometry of the patients' anatomy.Conclusions.The proposed linear network-based optimization model efficiently generates more homogeneous high quality treatment plans for HDR-BT.

A posteriori error approximation in discontinuous Galerkin method on polygonal meshes in elliptic problems

The paper presents a posteriori error approximation concept based on residuals in the two-dimensional discontinuous Galerkin (DG) method. The approach is relatively simple and effective in application, and it takes advantage of some unique properties of the DG method. The error function is constructed in an enriched approximation space, utilizing the hierarchical nature of the basis functions. Among many versions of the DG method, the most popular one is based on the interior penalty approach. However, in this paper a DG method with finite difference (DGFD) is utilized, where the continuity of the approximate solution is enforced by finite difference conditions applied on the mesh skeleton. In the DG methods arbitrarily shaped finite elements can be used, so in this paper the meshes with polygonal finite elements are considered, including quadrilateral and triangular elements. Some benchmark examples are presented, in which Poisson’s and linear elasticity problems are considered. The examples use various mesh densities and approximation orders to evaluate the errors. The error estimation maps, generated for the discussed tests, indicate a good correlation with the exact errors. In the last example, the error approximation concept is applied for an adaptive hp mesh refinement.

Priority Criteria (PC) Based Particle Swarm Optimization of Reinforced Concrete Frames (PCPSO)

Simplicity and optimality are commonly associated with the particle swarm optimization (PSO) algorithm. As a result, numerous variants and hybrids of PSO have been developed and implemented to address structural optimization problems. The undeniable importance of the initialization technique in determining the overall performance of a given optimization algorithm cannot be overstated. Optimization algorithms, such as PSO, typically rely on a random, uniformly distributed initialization. Through multiple iterations and updates, these algorithms aim to achieve optimal results. The underlying assumption behind such an initialization approach is that a fair or reasonable arrangement of particles is best accomplished through randomization, and thus the entire optimization process is iterated based on this assumption. However, this initialization technique raises concerns regarding the attainment of optimality and convergence, leaving room for further examination. In this paper, we challenge this assumption by introducing a priority concept. The key idea is that particles should not be initialized randomly since randomness alone does not guarantee a reasonable allocation of design variable values in iterative optimization. This can lead to misguided velocity updates and ultimately, a time-consuming pursuit of optimality. To address this issue, we formulate priority criteria (PC) and propose an enhanced PSO variant called Priority Criteria PSO (PCPSO). The PC can be incorporated into any PSO variant or hybrid without impacting the parameter settings, constraints, and penalty approaches of the respective algorithms. A case study involving 2D reinforced concrete frames was conducted to compare the performance of the ordinary PSO algorithm with the PCPSO. The results clearly demonstrate that the introduction of the PC leads to a significant cost reduction when compared to PSO with an inertia damping factor. Additionally, the PCPSO algorithm exhibits accelerated convergence. Furthermore, to alleviate the computational burden associated with structural analysis at each iteration, a reanalysis approach called Combined Approximations (CA) is mathematically formulated and implemented.

Alternate Approach (Penalty Approach) to Assignment Problem Solving and Comparison to Existing Approaches

Assignment problem is an important area in Operation Research and is also discussed in real physical world. In this paper an attempt has been made to solve the assignment problem using a new Method called the Penalty method. We discuss a numerical example by using the new Method and compare it with standard existing method which is the Hungarian method. We compare the optimal solution of the new Method and the Hungarian method. The new method is a simple procedure, easy to apply for solving assignment problem.