Penalty Scheme Research Articles

Unified a priori analysis of four second-order FEM for fourth-order quadratic semilinear problems

A unified framework for fourth-order semilinear problems with trilinear nonlinearity and general sources allows for quasi-best approximation with lowest-order finite element methods. This paper establishes the stability and a priori error control in the piecewise energy and weaker Sobolev norms under minimal hypotheses. Applications include the stream function vorticity formulation of the incompressible 2D Navier-Stokes equations and the von Kármán equations with Morley, discontinuous Galerkin, C^{0} interior penalty, and weakly over-penalized symmetric interior penalty schemes. The proposed new discretizations consider quasi-optimal smoothers for the source term and smoother-type modifications inside the nonlinear terms.

Topology optimization of turbine disk considering maximum stress prediction and constraints

For the stress-constrained topology optimization of a turbine disk under centrifugal loads, the jagged boundaries of the mesh and the gray densities on the solid/void interfaces could make the calculated stress field inconsistent with the actual value. It may result in overestimating the maximum stress and thus affect the effectiveness of stress constraints. This paper proposes a new method for predicting the maximum stress to overcome the difficulty. In the process, a predicted density is newly defined to obtain stable boundaries with thin layers of gray elements, a transition factor is innovatively proposed to evaluate the effects of intermediate-density elements, two different stiffness penalty schemes are flexibly used to calculate the elastic modulus of elements, and a linear stress penalty is further adopted to relax the stress field of the structure. The proposed approach for predicting the maximum stress value is verified by the analysis of a structure with smooth boundaries and the topology optimization of a turbine disk. An updating scheme of the stress constraint in the topology optimization is also developed using the predicted maximum stress. Some key ingredients affecting the optimization results are discussed in detail. The results prove the effectiveness and efficacy of the proposed maximum stress prediction and developed stress constraint methods.

Comparing Canada's proposed Critical Cyber Systems Protection Act with cybersecurity legal requirements in the EU.

This article examines the Canadian federal government's proposed Critical Cyber Systems Protection Act (CCSPA), compares it with existing and proposed cybersecurity legal requirements in the European Union (EU), and sets out recommendations to address shortcomings of the proposed Canadian legislation. One of the cornerstone components of Bill C‑26, the CCSPA seeks to regulate critical cyber systems in federally regulated private sectors. It represents asignificant overhaul of Canadian cybersecurity regulation. However, the current proposed legislation exhibits many flaws, including acommitment to, and entrenchment of, apatchwork approach to regulation that focuses on formal registration; alack of oversight of its confidentiality provisions; aweak penalty scheme that focuses solely on compliance, not deterrence; and diluted conduct, reporting, and mitigation obligations. To repair these flaws, this article reviews the provisions of the proposed law and compares them with the EU's Directive Concerning Measures for aHigh Common Level of Security of Network and Information Systems Across the Union, the first EU-wide cybersecurity legislation, as well as its proposed successor, the NIS2 Directive. Where relevant, various other cybersecurity regulations in peer states are discussed. Specific recommendations are put forward.

Solving Two-Person Zero-Sum Stochastic Games With Incomplete Information Using Learning Automata With Artificial Barriers.

Learning automata (LA) with artificially absorbing barriers was a completely new horizon of research in the 1980s (Oommen, 1986). These new machines yielded properties that were previously unknown. More recently, absorbing barriers have been introduced in continuous estimator algorithms so that the proofs could follow a martingale property, as opposed to monotonicity (Zhang et al., 2014), (Zhang et al., 2015). However, the applications of LA with artificial barriers are almost nonexistent. In that regard, this article is pioneering in that it provides effective and accurate solutions to an extremely complex application domain, namely that of solving two-person zero-sum stochastic games that are provided with incomplete information. LA have been previously used (Sastry et al., 1994) to design algorithms capable of converging to the game's Nash equilibrium under limited information. Those algorithms have focused on the case where the saddle point of the game exists in a pure strategy. However, the majority of the LA algorithms used for games are absorbing in the probability simplex space, and thus, they converge to an exclusive choice of a single action. These LA are thus unable to converge to other mixed Nash equilibria when the game possesses no saddle point for a pure strategy. The pioneering contribution of this article is that we propose an LA solution that is able to converge to an optimal mixed Nash equilibrium even though there may be no saddle point when a pure strategy is invoked. The scheme, being of the linear reward-inaction ( LR-I ) paradigm, is in and of itself, absorbing. However, by incorporating artificial barriers, we prevent it from being "stuck" or getting absorbed in pure strategies. Unlike the linear reward- ϵ penalty ( LR-ϵP ) scheme proposed by Lakshmivarahan and Narendra almost four decades ago, our new scheme achieves the same goal with much less parameter tuning and in a more elegant manner. This article includes the nontrial proofs of the theoretical results characterizing our scheme and also contains experimental verification that confirms our theoretical findings.

Unifying a posteriori error analysis of five piecewise quadratic discretisations for the biharmonic equation

Abstract An abstract property (H) is the key to a complete a priori error analysis in the (discrete) energy norm for several nonstandard finite element methods in the recent work [Lowest-order equivalent nonstandard finite element methods for biharmonic plates, Carstensen and Nataraj, M2AN, 2022]. This paper investigates the impact of (H) to the a posteriori error analysis and establishes known and novel explicit residualbased a posteriori error estimates. The abstract framework applies to Morley, two versions of discontinuous Galerkin, C 0 interior penalty, as well as weakly overpenalized symmetric interior penalty schemes for the biharmonic equation with a general source term in H −2(Ω).

The effect of transparent unequal penalty rates on safety compliance for different-sized businesses

Abstract The present study investigates the relation of procedural transparency and compliance with authorities’ regulations. The underlying assumption is that procedural transparency encourages compliance with regulations. In an incentivized experiment, 666 participants took on the role of a business owner and had to fill in a form and spend a certain amount of their income as compliance costs to adhere to safety rules. In a 2 (Business Size: small vs big) × 2 (Penalty Rate: equal vs unequal) × 2 (Penalty Scheme: transparent vs nontransparent) between-subjects design, we investigated whether an unequal penalty rate for small-size in contrast to big-size businesses had a different effect on compliance when this difference was transparent compared to when it was not transparent. Business income, compliance costs, and audit probability were varied within-subject, over 18 decision rounds. We find that the deterring effect of a higher penalty rate for big-size compared to small-size businesses under a nontransparent unequal penalty scheme is attenuated when the same information is available. This supports the idea of a backfiring effect and suggests that authorities need to carefully consider what information about their procedures to communicate in order to avoid the unintended negative effects of increasing transparency.

Learning an attention-aware parallel sharing network for facial attribute recognition

Existing multi-task learning based facial attribute recognition (FAR) methods usually employ the serial sharing network, where the high-level global features are used for attribute prediction. However, the shared low-level features with valuable spatial information are not well exploited for multiple tasks. This paper proposes a novel Attention-aware Parallel Sharing network termed APS for effective FAR. To make full use of the shared low-level features, the task-specific sub-networks can adaptively extract important features from each block of the shared sub-network. Furthermore, an effective attention mechanism with multi-feature soft-alignment modules is employed to evaluate the compatibility of the local and global features from the different network levels for discriminating attributes. In addition, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the problems of class imbalance and hard example mining for FAR. Experiments demonstrate that the proposed method achieves better performance than the state-of-the-art FAR methods.

Name2Vec: Name Matching using Character-based with Deep Learning

Name matching plays a crucial role in big data and various integration applications, being indispensable when consolidating information from diverse sources. This encompasses tasks such as deduplication, data linkage systems, search engines, text and web mining, information extraction, and more. Discrepancies and anomalies in names, including syntax variations like abbreviations, typographical errors, occasional whitespace omissions, word insertions, deletions, and even multiple spellings for the same name, can lead to missed matches. In previous methodologies, a predefined penalty scheme was often employed for each differing character or multi-character token between two strings. This research introduces Name2Vec, an algorithm that addresses name matching using a neural network model to capture name semantics. This approach advances by suggesting a suitable feature set through the fusion of Name2Vec and character-based name representations. The empirical findings of this research confirm that this performance enhancement improves matching efficiency while simultaneously reducing misclassifications compared to state-of-the-art methods.

Entropy generation analysis during MHD mixed convection flow of non-Newtonian fluid saturated inside the square cavity

The present computational study consists of the analysis of energy transport and entropy generation due to MHD mixed convective flow of non-Newtonian fluid filled inside the square cavity. The uniform, linear heat is provided to the fluid through the horizontal bottom and vertical side walls, while considering the top wall of the cavity as adiabatic. The governing flow equations are modeled for non-Newtonian bi-viscous fluid model by using the conservation laws. Galerkin-finite element method (FEM) is used to compute the solution of the governing problem and pressure is eliminated through penalty scheme. Furthermore, two-dimensional convective heat flow is identified by using the idea of Bejan heatlines. Numerical results are computed against wide range of pertinent flow control parameters such as bi-viscosity (β), Prandtl (Pr), Richardson (Ri), Reynolds (Re) and Grashof numbers (Gr). Results show that the fluid velocity, energy transfer rate and entropy generation are increased due to enhancement in bi-viscosity parameter (β). On the other side, the velocity and temperature are decreased by increasing Hartmann number (Ha), however the entropy generation increased. Hence, the minimum entropy generation is reported at β=0.002, Ha=100 and Ri=0.1, respectively.

Evolutionary game theory-based system dynamics modeling for community solid waste classification in China

To increase the utilization rate of renewable resources, China will widely implement solid waste classification in the next few years. However, waste classification in many communities has been placed on hold. This study establishes a multi-player evolutionary game among the government, community, and residents to explore the real reasons for the implementation difficulties. The evolutionary game is simulated by adopting system dynamics to analyze the effectiveness of various strategies on the game process and game equilibrium to provide references for the government to formulate macro policies. We show that fluctuations in a static penalty scheme make formulating effective strategies difficult for the government. By contrast, a dynamic penalty scheme can effectively eliminate fluctuations. Furthermore, the optimal dynamic penalty-subsidy scheme features an ideal evolutionarily stable strategy where the optimal strategy for a community and its residents is to implement the waste classification system and obey the rules, respectively.

On Adaptive Grad-Div Parameter Selection

We propose, analyze and test a new adaptive penalty scheme that picks the penalty parameter $\epsilon$ element by element small where $\nabla\cdot u^h$ 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-$\epsilon$ 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.

Lagrangian meshfree particle method (SPH) based simulation for granular flow in a rotating drum with regularized μ(I) elastoplastic model

In this work, smoothed particle hydrodynamics (SPH) is employed to investigate the granular flow in a rotating drum. The granular material is modeled with a μI rheology-based elastoplastic model with the Druck-Prager yield surface. The μI relation is regularized with a penalty scheme to remove the singularity in the case of zero pressure or shear rate. Two benchmark problems, including the sand column collapse and the granular mixing due to pure convection, are simulated to examine the proposed SPH method. The numerical simulations are compared with available experimental and numerical results. A series of numerical simulations are conducted to study the behavior of the granular bed in a rotating drum. It turns out the proposed SPH method is a promising tool to reveal the characteristics of flow modes, bed profile, velocity field and stress distribution.

Encouraging preventive services in primary care through payment schemes

Strengthening the use of preventive services in primary care has become the key to solving the problem of a global increase in healthcare expenditures caused by aging populations and chronic diseases. In practice, whether devoting effort to preventive services can be a strategic decision when the limited working time keeps a general practitioner (GP) busy with diagnosis and treatment. Based on the above motivations and challenges, we are the first to focus on the conflict between treatment services and preventive services of a GP with limited service capacity in the healthcare field. This paper considers a public health system consisting of a funder, a GP, a specialist, and a pool of delay-sensitive residents. Four payment schemes are investigated in terms of encouraging the GP to deliver preventive services: (i) fee for service (FFS), (ii) capitation, (iii) GP fundholding, and (iv) penalty scheme. The result shows that the GP fundholding scheme and the penalty scheme are more effective in encouraging preventive service provision in primary care, whereas the other two schemes may result in underinvestment in preventive services. Further, we compare the performance of the GP fundholding scheme and penalty scheme by applying the optimal reimbursement, which refers to the amount that maximizes social welfare. Compared with the GP fundholding scheme, the penalty scheme can incentivize preventive services with lower expenditures for the funder. There are two additional interesting findings: (1) A larger reimbursement discourages the GP from delivering preventive services. (2) An increased marginal treatment cost always incentivizes the GP to allocate more working time to preventive services, thereby reducing the referral rate and saving costs. Counterintuitively, increasing the marginal cost of preventive services may also encourage the GP to deliver preventive services. Our analysis sheds light on how to encourage preventive service provision in primary care through payment schemes.

Stick or carrot for traffic demand management? Evidence from experimental economics

The debate between stick (penalties) and carrot (rewards) strategies has been widely carried out in various academic realms, including transportation. This paper seeks to compare the instantaneous and longitudinal effectiveness of a traditional penalty-based congestion pricing strategy and an innovative Incentive-Based Traffic Demand Management (IBTDM) strategy using an online interactive laboratory experiment focused on departure time choice. We also investigate the social-economic phenomena of loss aversion and risk aversion. The experimental results show that penalties and rewards can both positively induce sustainable travel behavior. The stronger demand-shifting performance of the penalty-based strategy at first demonstrates that ‘loss aversion’ does exist in TDM, at least initially. However, this loss aversion weakens as the experiment continues, which verifies the myopic loss aversion theory. Moreover, risky decision-making in the gambling games proves the existence of risk seeking, which violates the ‘certainty effect’. To overcome the disadvantage of laboratory experiment that participants need to accomplish the experiment all in once for considerably long time, an agent-based model is calibrated based on real participants’ behavior to further verify the long-term effectiveness of penalties and rewards, and to verify the effectiveness of other optimized penalty/reward schemes. The simulated results based on the agent-based model indicate that the theoretical optimal penalty scheme performs better in general in congestion alleviation, while a more reasonable reward scheme that considers psychological factors can achieve the same effectiveness in terms of total system travel time reduction. The findings of this research can be used to guide the design of TDM strategies, and the behavioral data collection approach based on our laboratory experiment is portable for future studies.

E‐commerce supply chains with considerations of cyber‐security: Should governments play a role?

E‐commerce supply chains and their members face risks from cyber‐attacks. Consumers who purchase goods online also risk having their private information stolen. Thus, businesses are investing to improve cyber‐security at a nontrivial cost. In this paper, we conduct a Stackelberg game‐theoretical analysis. In the basic model, we first derive the equilibrium pricing and cyber‐security level decisions in the e‐commerce supply chain. Based on real‐world practices, we then explore whether governments should impose cyber‐security penalty schemes. Our findings show that when the government is characterized by having sufficiently high emphasis on consumer surplus, implementing the penalty scheme is beneficial to social welfare. Then, we extend the analysis to examine how adopting systems security enhancing technologies (such as blockchain) will affect the government's choice of imposing penalty. We uncover that when it is beneficial to have government's penalty scheme, the technology benefit‐to‐cost ratio is a critical factor that governs whether the optimal penalty will be lower or higher with the adoption of systems security enhancing technologies. To generate more insights, we conduct further analyses for various extended modeling cases (e.g., with alliance, competition, and the defense‐level dependent penalty scheme) and find that our main results remain robust. One important insight we have uncovered in this study is that imposing government penalty schemes on cyber‐security issues may do more harm than good; while once it is beneficial to implement, the government should charge the heaviest possible fine. This finding may explain why in the real world, governments basically always adopt a polarized strategy, that is, either do not impose penalty or impose a super heavy penalty, on cyber‐security issues.

Effectiveness of Extrinsic Incentives for Promoting Rural Waste Sorting in Developing Countries: Evidence from China

Rural areas in many developing countries lack an effective waste management system, leading to poor environmental hygiene. As an initial but integral step toward sustainable waste management, fostering better household compliance with waste‐sorting regulation has become an important policy agenda. This paper devotes attention to evaluating the effectiveness of extrinsic incentives, more specifically, reward–penalty schemes, on promoting rural households' participation in waste sorting. By exploiting an original survey of 1,058 rural households conducted in the Taihu Basin of China, we empirically find that (1) reward–penalty scheme considerably promotes rural households' engagement with waste sorting; (2) the more stringently the scheme is implemented, the higher the probabilities of more frequent sorting; and (3) point reward, which can be exchanged for vouchers used in local stores, is the most effective extrinsic incentive for promoting waste sorting. The findings provide insights for enhancing rural waste sorting in the developing world.

Spin Purification in Full-CI Quantum Monte Carlo via a First-Order Penalty Approach.

In this article, we demonstrate that a first-order spin penalty scheme can be efficiently applied to the Slater determinant based Full-CI Quantum Monte Carlo (FCIQMC) algorithm, as a practical route toward spin purification. Two crucial applications are presented to demonstrate the validity and robustness of this scheme: the 1Δg ← 3Σg vertical excitation in O2 and key spin gaps in a [Mn3(IV)O4] cluster. In the absence of a robust spin adaptation/purification technique, both applications would be unattainable by Slater determinant based ground state methods, with any starting wave function collapsing into the higher-spin ground state during the optimization. This strategy can be coupled to other algorithms that use the Slater determinant based FCIQMC algorithm as configuration interaction eigensolver, including the Stochastic Generalized Active Space, the similarity-transformed FCIQMC, the tailored-CC, and second-order perturbation theory approaches. Moreover, in contrast to the GUGA-FCIQMC technique, this strategy features both spin projection and total spin adaptation, making it appealing when solving anisotropic Hamiltonians. It also provides spin-resolved reduced density matrices, important for the investigation of spin-dependent properties in polynuclear transition metal clusters, such as the hyperfine-coupling constants.

Domestic wastewater in Indonesia: generation, characteristics and treatment.

With a 270 million Indonesian population, domestic wastewater is one of the major contributors to wastewater generated from human activities. This review aimed to give an overview of the current state of domestic wastewater generation, characteristics and treatment systems in Indonesia. Overall, grey water quantity in Indonesia was 1 to 4 times higher than black water quantity, while the quantity of untreated grey water was 3 to 6 times higher than untreated black water. Parameters of concern include suspended solids, biochemical oxygen demand, chemical oxygen demand, oil and grease, nitrogen and coliforms. Our analysis shows that grey water can be a significant source of water pollution due to the large quantity and lack of treatment. In addition, black water treatment that relies mainly on on-site treatment is often inadequate due to the lack of quality control for the infrastructure, operation and maintenance. An incentive or penalty scheme to build and ensure the quality of domestic wastewater treatment is required and can be applied at the household, community or central (city) level.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-19057-6.

Deep Neural Network Model to Predict the Electrostatic Parameters in the Polarizable Classical Drude Oscillator Force Field.

The Drude polarizable force field (FF) captures electronic polarization effects via auxiliary Drude particles that are attached to non-hydrogen atoms, distinguishing it from commonly used additive FFs that rely on fixed charges. The Drude FF currently includes parameters for biomolecules such as proteins, nucleic acids, lipids, and carbohydrates and small-molecule representative of those classes of molecules as well as a range of atomic ions. Extension of the Drude FF to novel small druglike molecules is challenging as it requires the assignment of partial charges, atomic polarizabilities, and Thole scaling factors. In the present article, deep neural network (DNN) models are trained on quantum mechanical (QM)-based partial charges and atomic polarizabilities along with Thole scale factors trained to target QM molecular dipole moments and polarizabilities. Training of the DNN model used a collection of 39 421 molecules with molecular weights up to 200 Da and containing H, C, N, O, P, S, F, Cl, Br, or I atoms. The DNN model utilizes bond connectivity, including 1,2, 1,3, 1,4, and 1,5 terms and distances of Drude FF atom types as the feature vector to build the model, allowing it to capture both local and nonlocal effects in the molecules. Novel methods have been developed to determine restrained electrostatic potential (RESP) charges on atoms and external points representing lone pairs and to determine Thole scale factors, which have no QM analogue. A penalty scheme is devised as a performance predictor of the trained model. Validation studies show that these DNN models can precisely predict molecular dipole and polarizabilities of Food and Drug Administration (FDA)-approved drugs compared to reference MP2 calculations. The availability of the DNN model allowing for the rapid estimation of the Drude electrostatic parameters will facilitate its applicability to a wider range of molecular species.

OPTIMAL MANAGEMENT OF WIND POWER PRODUCTION VIA DISPATCHABLE ENERGY SOURCES

In this paper, a nonlin ear optimization problem for the optimal management of wind energy using dispatchable energy sources (DES) is solved. The model accounts for several aspects such as a penalty scheme in the wind power underproduction’s case and the modeling of both electricity prices and wind energy production. All the quantities involved in the model are commented and computed through an empirical investigation based on a supposed wind farm.