Search Results (44)

A Bonus–Malus System (BMS) is a ratemaking mechanism used in insurance to adjust premiums based on a policyholder’s claim history, with the goal of segmenting risk profiles more accurately. A BMS typically comprises three key components: the number of BMS levels, the transition rules dictating the movements of policyholders within the system, and the relativities used to determine premium adjustments. This paper explores the impact of modifications to these three elements on risk classification, assessed through the mean squared error. The model parameters are calibrated with real-world data from the Saudi auto insurance market. We begin the analysis by focusing on transition rules based solely on claim frequency, a framework in which most implemented BMSs work, including the current Saudi BMS. We then consider transition rules that depend on frequency and severity, in which higher penalties are given for large claim sizes. The results show that increasing the number of levels typically improves risk segmentation but requires balancing practical implementation constraints and that the adequate selection of the penalties is critical to enhancing fairness. Moreover, the study reveals that incorporating a severity-based penalty enhances risk differentiation, especially when there is a dependence between the claim frequency and severity. Full article

Task scheduling is a critical challenge in cloud computing systems, greatly impacting their performance. Task scheduling is a nondeterministic polynomial time hard (NP-Hard) problem that complicates the search for nearly optimal solutions. Five major uncertainty parameters, i.e., security, traffic, workload, availability, and price, influence task scheduling decisions. The primary rationale for selecting these uncertainty parameters lies in the challenge of accurately measuring their values, as empirical estimations often diverge from the actual values. The integral-valued Pythagorean fuzzy set (IVPFS) is a promising mathematical framework to deal with parametric uncertainties. The Dyna Q+ algorithm is the updated form of the Dyna Q agent designed specifically for dynamic computing environments by providing bonus rewards to non-exploited states. In this paper, the Dyna Q+ agent is enriched with the IVPFS mathematical framework to make intelligent task scheduling decisions. The performance of the proposed IVPFS Dyna Q+ task scheduler is tested using the CloudSim 3.3 simulator. The execution time is reduced by 90%, the makespan time is also reduced by 90%, the operation cost is below 50%, and the resource utilization rate is improved by 95%, all of these parameters meeting the desired standards or expectations. The results are also further validated using an expected value analysis methodology that confirms the good performance of the task scheduler. A better balance between exploration and exploitation through rigorous action-based learning is achieved by the Dyna Q+ agent. Full article

Little research has been done on professionals’ perceptions of institutions and governments during epidemics. We aim to create a profile of physicians who feel they can raise public health issues with relevant institutions during a pandemic. A total of 1285 Romanian physicians completed an online survey as part of a larger study. We used binary logistic regression to profile physicians who felt they were able to raise public health issues with relevant institutions. Five predictors could differentiate between respondents who tended to agree with the trust statement and those who tended to disagree: feeling safe at work during the pandemic, considering the financial incentive worth the risk, receiving training on the use of protective equipment, having the same values as colleagues, and enjoying work as much as before the pandemic. Physicians who trusted the system to raise public health issues with the appropriate institutions were more likely to feel that they shared the same values as their colleagues, to say they were trained to use protective equipment during the pandemic, to feel that they were safe at work during the pandemic, to enjoy their work as much as before the pandemic, and to feel that the financial bonus justified the risk. Full article

With the development of the digital economy, the algorithms and big data technologies of e-commerce platforms have gradually turned into double-edged swords. While realising personalised recommendations, they also provide information technology support for the use of algorithmic prices to discriminate and extract residual value from consumers. Consumers frequently use Black Cat and third-party media to complain, resulting in a significant negative impact. Therefore, in order to regulate algorithmic price discrimination, using e-commerce platforms, local governments and consumers act as game subjects, taking an evolutionary game approach. We analyse the impact of different situations and factors on the system’s evolutionary stability strategy and conduct its verification via simulation experiments. This study shows that several measures, such as increasing cooperation with the media; establishing clear regulatory rules to reduce the extent of algorithmic price discrimination and the grey revenue of e-commerce platforms; establishing a long-term mechanism for consumer feedback; improving rewards and punishments to increase the probability of successful regulation and penalties by local governments; sharing information to reduce the cost of consumer regulation; and setting reasonable bonus thresholds based on government revenue and consumer regulation costs, can effectively regulate algorithmic price discrimination and promote the sustainable development of e-commerce platforms. Full article

The field of energy harvesting is expanding to power various devices, including electric vehicles, with energy derived from their surrounding environments. The unique mechanical and electrical qualities of composite materials make them ideal for energy harvesting applications, and they have shown tremendous promise in this area. Yet additional studies are needed to fully grasp the promise of composite materials for energy harvesting in electric vehicles. This article reviews composite materials used for energy harvesting in electric vehicles, discussing mechanical characteristics, electrical conductivity, thermal stability, and cost-effectiveness. As a bonus, it delves into using composites in piezoelectric, electromagnetic, and thermoelectric energy harvesters. The high strength-to-weight ratio provided by composite materials is a major benefit for energy harvesting. Especially important in electric vehicles, where saving weight means saving money at the pump and driving farther between charges, this quality is a boon to the field. Many composite materials and their possible uses in energy harvesting systems are discussed in the article. These composites include polymer-based composites, metal-based composites, bio-waste-based hybrid composites and cement-based composites. In addition to describing the promising applications of composite materials for energy harvesting in electric vehicles, the article delves into the obstacles that must be overcome before the technology can reach its full potential. Energy harvesting devices could be more effective and reliable if composite materials were cheaper and less prone to damage. Further study is also required to determine the durability and dependability of composite materials for use in energy harvesting. However, composite materials show promise for energy harvesting in E.V.s. Further study and development are required before their full potential can be realized. This article discusses the significant challenges and potential for future research and development in composite materials for energy harvesting in electric vehicles. It thoroughly evaluates the latest advances and trends in this field. Full article

The current study investigates and establishes the factors that lead to short-term orientation strategies in western organisations. Moreover, this study used questionnaire-based surveys to assess the relationship between these factors and what it would take for the organisation to move to a long-term orientation strategy. At the start of the study, it was evident that the annual bonus system is a key reason for prioritising a short-term orientation strategy. The sample was composed of 300 companies of different sizes from different western countries. Furthermore, the general consensus within these companies was that most managers knew that long-term orientation was necessary; however, either their superiors/shareholders/supervisory board only reward short-term results, or the managers and stakeholders prefer short-term goals for evaluations because it is challenging to transform a long-term strategy into multiple short-term strategies and goals. At the end of the study, a basic framework has been suggested to be used as guidelines for any company moving from short-term to long-term orientation strategies. Full article

This paper proposes that balancing public and private satisfaction in the creation of privately owned public space (POPS) might be the key to producing more efficient and effective POPSs. Seven qualitative techniques categorized into on-site observations, space syntax, survey, and regulatory review were used to gather data, and triangulation methods were used to derive conclusions. We then discussed methods for improving POPS planning and designs that prioritize both public and private sectors by assessing the indirect and direct benefits of POPS. Indirect benefits are delivered when POPS can elevate the pleasantness of the surrounding environment. In this study, users and tenants of the host buildings were found to obtain the most benefits with their easy access to POPS, while the public saw the existence of POPS as insignificant compared to local parks. Furthermore, the lack of good designs resulted in low public interest and awareness. Although developers gain direct benefits from bonus FAR, a less rigid but more comprehensive system is needed to increase developer motivation to create better POPS. Proposals that combine two or more POPS and regulations that require connection to existing public open space networks should be considered in future. Full article

In this research, we introduce an artificial gorilla troop optimizer for use in artificial neural networks that manage energy consumption in DC–AC hybrid distribution networks. It is being proposed to implement an energy management system that takes into account distributed generation, load demand, and battery-charge level. Using the profile data, an artificial neural network was trained on the charging and discharging characteristics of an energy storage system under a variety of distribution-network power situations. As an added bonus, the percentage of mistakes was maintained far below 10%. An artificial neural network is used in the proposed energy management system, and it has been taught to operate in the best possible manner by using an optimizer inspired by gorillas called artificial gorilla troops. The artificial gorilla troops optimizer optimize the weights and bias of the neural network based on the power of the distributed generator, the power of the grid, and the reference direct axis current to obtain most suitable energy management system. In order to simulate and evaluate the proposed energy management system, small-scale hybrid DC/AC microgrids have been created and tested. When compared to other systems in the literature, the artificial gorilla troops optimizer enhanced neural network energy management system has been shown to deliver 99.55% efficiency, making it the clear winner. Full article

In this paper, we use EUROMOD, the tax-benefit microsimulation model of the European Union, to investigate the impact of marriage-related tax-benefit instruments on the financial incentives to work of married couples. For each spouse, we estimate their individual marginal effective tax rate and net replacement rate before and after marriage. We show that the marriage bonus, which is economically significant in eight European countries, decreases the work incentives for women, particularly on the intensive margin. In contrast, for men the incentives on the intensive margin increase once they are married, pointing to the marriage-biased and gender-biased tax-benefit structures in the analysed countries. Our results suggest that marriage bonuses contribute to a lock-in effect, where second earners, typically women, are incentivised to work less, with negative economic consequences. Full article

The bonus-malus system (BMS) is one of the most widely used tools in merit-rating automobile insurance, with the primary goal of ensuring that fair premiums are paid by all policyholders. The traditional BMS is dependent only on the claim frequency. Thus, an insured person who makes a claim with a small severity is penalized unfairly compared to an individual who makes a large severity claim. This study proposes a model for estimating the bonus-malus premium by employing a limit value (monetary unit) which distinguishes claim size into small and large based on claim frequency and claim severity distributions. This assists in determining the penalties for policyholders with claim sizes falling above and below the limit value. The number of claims is assumed to follow a Poisson distribution, and the total number of claims with a size greater than the limit value is considered a binomial distribution. The underlying risk of each policyholder is assumed to follow a beta Lindley distribution and is referred to as the prior distribution. Each policyholder’s claim size is also assumed to follow a gamma distribution, with the Lindley distribution considered as the prior distribution. Bonus-malus premiums are calculated following the Bayesian method. Practical examples using an actual data set are provided, and the results generated are compared to those produced using the traditional Poisson binomial-exponential beta model. This methodology provides a more equitable mechanism for penalizing policyholders in the portfolio. Full article

Introduction: The optimal freeze duration in cryoballoon pulmonary vein isolation (PVI) is unknown. TTI-based titration of cryoenergy allows individualized freeze duration and has emerged as a favorable ablation strategy in PV cryoablation. In a recent study, we demonstrated that omission of a bonus freeze and reduction in freeze duration to a minimum of 2 min in the case of short TTI led to comparable arrhythmia recurrence rates. Whereas clinical outcome seems to be comparable to fixed freeze duration, evidence of long-term PV reconnection rates in patients undergoing TTI-based cryoballoon ablation is sparse. Aim of the study: To evaluate the procedural efficacy of a single 2-min freeze for PVI, we assessed PV conduction recovery after cryoballoon PVI with a TTI-guided titration of freeze duration compared to a fixed ablation protocol. Methods and Results: We included consecutive patients with atrial fibrillation (AF) recurrence undergoing a second ablation procedure after the initial cryoballoon procedure. The second AF ablation procedure was performed by the 3D-mapping system and radiofrequency ablation technique. A total of 219 patients (age: 66.2 ± 10.8 years, 53% female, paroxysmal AF: 53%) treated with the TTI-guided protocol (174 patients, 685 PV) or fixed protocol (45 patients, 179 PV) showed comparable total reconnection rates (TTI: 36.9% vs. fixed: 31.8%, p = 0.21). The PV reconnection rate was not statistically different for PVs treated with a 2-min freeze in case of short TTI, compared to longer freeze duration. Interestingly, the PV reconnection rate was lower in LIPVs treated with the fixed protocol (13% vs. 31%, p = 0.029). In the TTI group, 17 out of 127 patients (15%) had durable isolation of all PVs, whereas in 8 out of 40 patients (20%) in the fixed group, all PVs were still isolated (p = 0.31). Conclusions: overall reconnection rate was not different using a TTI-guided ablation protocol compared to a fixed ablation protocol, whereas the LIPV reconnection rate was significantly lower in patients treated with a fixed ablation protocol. Full article

How to consider the a priori risks in experience-rating models has been questioned in the actuarial community for a long time. Classic past-claim-rating models, such as the Buhlmann–Straub credibility model, normalize the past experience of each insured before applying claim penalties. On the other hand, classic Bonus–Malus Scales (BMS) models generate the same surcharges and the same discounts for all insureds because the transition rules within the class system do not depend on the a priori risk. Despite the quality of prediction of the BMS models, this experience-rating model could appear unfair to many insureds and regulators because it does not recognize the initial risk of the insured. In this paper, we propose the creation of different BMSs for each type of insured using recursive partitioning methods. We apply this approach to real data for the farm insurance product of a major Canadian insurance company with widely varying sizes of insureds. Because the a priori risk can change over time, a study of the possible transitions between different BMS models is also performed. Full article

One of the main objectives of new operating regimes, such as transactional power systems, is to make the power grid more resilient to catastrophes and disturbances, while at the same time making it possible to supply electricity to the largest number of customers. Although this is true, it is well known among power system operators that not all customers are the same. The consequence of this is that any discussion around the impact of transactional power systems on power system resilience should consider the needs of its critical users (such as control centers, fire stations, and hospitals) over other users. In assessing power system resilience, a metric is needed that gives “bonus points” to those systems that, under all circumstances, can continue to provide electricity to their critical users. In order to serve as a parameter in the assessment of power grid resilience, the research presented here discusses the proportion of critical loads existing in critical infrastructures. Once the critical loads are characterized, the next step is the inclusion of these loads in resilience metrics. This paper proposes resiliency metrics in which certain customers (those categorized as critical) are assigned a higher weight than others. One thing to keep in mind is the fact that there is no one-size-fits-all approach for all power systems, and that the assignment of such weights to customers can vary significantly from one operator to another based on their unique systems and the current and expected states of their critical customers. Full article

Smart grid technology has gained much consideration recently to make use of intelligent control in the automatic fault-detection and self-healing of electric networks. This ensures a reliable electricity supply and an efficient operation of the distribution system against disasters with minimum human interaction. In this paper, a fully decentralized multi-agent system (MAS) algorithm, for self-healing in smart distribution systems, is proposed. The novelty of the proposed algorithm, compared to related work, is its ability to combine the zone and feeder agents, specified for system self-healing, with micro-grid agents. This enables the system to successfully achieve functions of fault locating and isolation along with service-restoration using expert rules while considering both operational constraint and load priorities. Meanwhile, managing the power flow and controlling the distributed generator (DG) contribution, in the considered network, is a bonus merit for the proposed algorithm. Hence, system self-healing as well as strengthening energy security and resiliency are guaranteed. The proposed algorithm is tested on a 22 kV radial distribution system through several case-studies with/without a DG wind-energy source. The employed agents are implemented in the Java Agent Developing Framework (JADE) environment to communicate and make decisions. Power system simulation and calculations are carried out in MATLAB to validate the agents’ decisions. Full article

This paper addresses the conditions that can facilitate the long-term effectiveness of civic crowdfunding fundraising strategies. While previous studies have provided a broad picture of the possible conditions for fostering effective fundraising strategies, most have considered the implications of fundraising only for management or only for cultural policy, neglecting an integrated approach that contemplates the needs of both. Thus, this work integrates cultural management and cultural policy perspectives by discussing a specific exploratory case study: Art Bonus, a cultural patronage tax incentive strategy introduced by the Italian government in 2014, which also includes civic crowdfunding features. To the best of our knowledge, Art Bonus is the first national civic crowdfunding platform supported by a national government. As an innovative and unique platform, its analysis is particularly relevant. This work analyzes the system’s functioning and the results obtained in its first years of operation (2014–2016) by accessing the public database relating to the donations transited through the platform. While the initiative effectively channeled more fundraising resources into the cultural sector, the results also illustrate potential points for improving such a system. Full article