Yang Zhao, Jin-Ping Lee and Min-Teh Yu
Catastrophe (CAT) events associated with natural catastrophes and man-made disasters cause profound impacts on the insurance industry. This research thus reviews the impact of CAT…
Abstract
Purpose
Catastrophe (CAT) events associated with natural catastrophes and man-made disasters cause profound impacts on the insurance industry. This research thus reviews the impact of CAT risk on the insurance industry and how traditional reinsurance and securitized risk-transfer instruments are used for managing CAT risk.
Design/methodology/approach
This research reviews the impact of CAT risk on the insurance industry and how traditional reinsurance and securitized risk-transfer instruments are used for managing CAT risk. Apart from many negative influences, CAT events can increase the net revenue of the insurance industry around CAT events and improve insurance demand over the post-CAT periods. The underwriting cycle of reinsurance causes inefficiencies in transferring CAT risks. Securitized risk-transfer instruments resolve some inefficiencies of the reinsurance market, but are subject to moral hazard, basis risk, credit risk, regulatory uncertainty, etc. The authors introduce some popular securitized solutions and use Merton's structural framework to demonstrate how to value these CAT-linked securities. The hybrid solutions by combining reinsurance with securitized CAT instruments are expected to offer promising applications for CAT risk management.
Findings
The authors introduce some popular securitized solutions and use Merton's structural framework to demonstrate how to value these CAT-linked securities. The hybrid solutions by combining reinsurance with securitized CAT instruments are expected to offer promising applications for CAT risk management.
Originality/value
This research reviews a broad array of impacts of CAT risks on the (re)insurance industry. CAT events challenge (re)insurance capacity and influence insurers' supply decisions and reconstruction costs in the aftermath of catastrophes. While losses from natural catastrophes are the primary threat to property–casualty insurers, the mortality risk posed by influenza pandemics is a leading CAT risk for life insurers. At the same time, natural catastrophes and man-made disasters cause distinct impacts on (re)insures. Man-made disasters can increase the correlation between insurance stocks and the overall market, and natural catastrophes reduce the above correlation. It should be noted that huge CAT losses can also improve (re)insurance demand during the postevent period and thus bring long-term effects to the (re)insurance industry.
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Zhao Yaoteng and Li Xin
The purpose of this paper is to explore the sustainable development strategy of green finance under the background of big data.
Abstract
Purpose
The purpose of this paper is to explore the sustainable development strategy of green finance under the background of big data.
Design/methodology/approach
From the perspective of big data, this paper uses quantitative and qualitative analysis methods to judge the correlation among green finance, environmental supervision and financial supervision. Green finance gives the entropy method to calculate the score of green finance and environmental regulation, and establishes the spatial lag model under the double fixed effects of time and space.
Findings
Spatial autocorrelation test shows that economic spatial weight matrix has obvious spatial effect on green innovation. Through the model selection test, the space lag model with fixed time and space is selected. The regression coefficients of green finance, environmental regulation and their interaction are 0.1598, 0.0541 and 0.1763, respectively, which significantly promote green innovation. The regression coefficients of openness, higher education level and per capita GDP are 0.0361, 0.0819 and 0.0686, respectively, which can significantly promote green innovation.
Originality/value
In view of the current situation of large-scale application of big data technology in green innovation of domestic energy-saving and environmental protection enterprises, this paper establishes a fixed time lag evaluation model of green innovation. M-test statistics show that the original hypothesis with no obvious spatial effect is rejected.
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Tuan Anh Nguyen and Jamshed Iqbal
Design a novel optimal integrated control algorithm for the automotive electric steering system to improve the stability and adaptation of the system.
Abstract
Purpose
Design a novel optimal integrated control algorithm for the automotive electric steering system to improve the stability and adaptation of the system.
Design/methodology/approach
Simulation and calculation.
Findings
The output signals follow the reference signal with high accuracy.
Originality/value
The optimal integrated algorithm is established based on the combination of PID and SMC. The parameters of the PID controller are adjusted using a fuzzy algorithm. The optimal range of adjustment values is determined using a genetic algorithm.
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Tingting Zhao, Y.T. Feng and Yuanqiang Tan
The purpose of this paper is to extend the previous study [Computer Methods in Applied Mechanics and Engineering 340: 70-89, 2018] on the development of a novel packing…
Abstract
Purpose
The purpose of this paper is to extend the previous study [Computer Methods in Applied Mechanics and Engineering 340: 70-89, 2018] on the development of a novel packing characterising system based on principal component analysis (PCA) to quantitatively reveal some fundamental features of spherical particle packings in three-dimensional.
Design/methodology/approach
Gaussian quadrature is adopted to obtain the volume matrix representation of a particle packing. Then, the digitalised image of the packing is obtained by converting cross-sectional images along one direction to column vectors of the packing image. Both a principal variance (PV) function and a dissimilarity coefficient (DC) are proposed to characterise differences between different packings (or images).
Findings
Differences between two packings with different packing features can be revealed by the PVs and DC. Furthermore, the values of PV and DC can indicate different levels of effects on packing caused by configuration randomness, particle distribution, packing density and particle size distribution. The uniformity and isotropy of a packing can also be investigated by this PCA based approach.
Originality/value
Develop an alternative novel approach to quantitatively characterise sphere packings, particularly their differences.
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Our editorial argues that categories theory can be advanced by embracing heuristics research, and the insight that audiences often evaluate items based on multiple valued…
Abstract
Our editorial argues that categories theory can be advanced by embracing heuristics research, and the insight that audiences often evaluate items based on multiple valued criteria. Thus, rather than building on extant theory – which suggests that categories embody specific evaluative criteria, or that audiences operate according to a set “theory of value” – the authors argue that hybrids research would benefit from attending to the underlying processes that actors use to weigh and balance the diverse considerations that guide their decisions. The authors define and discuss three commonly used heuristics (satisficing, lexicographic preferences, and elimination by aspects), and show how these might lead audiences to support different types of hybrid entities.
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Lijun Shang, Qingan Qiu, Cang Wu and Yongjun Du
The study aims to design the limited number of random working cycle as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product…
Abstract
Purpose
The study aims to design the limited number of random working cycle as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product reliability during the warranty period. By extending the proposed warranty to the consumer's post-warranty maintenance model, besides the authors investigate two kinds of random maintenance policies to sustain the post-warranty reliability, i.e. random replacement first and random replacement last. By integrating depreciation expense depending on working time, the cost rate is constructed for each random maintenance policy and some special cases are provided by discussing parameters in cost rates. Finally, sensitivities on both the proposed warranty and random maintenance policies are analyzed in numerical experiments.
Design/methodology/approach
The working cycle of products can be monitored by advanced sensors and measuring technologies. By monitoring the working cycle, manufacturers can design warranty policies to ensure product reliability performance and consumers can model the post-warranty maintenance to sustain the post-warranty reliability. In this article, the authors design a limited number of random working cycles as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product reliability performance during the warranty period. By extending a proposed warranty to the consumer's post-warranty maintenance model, the authors investigate two kinds of random replacement policies to sustain the post-warranty reliability, i.e. random replacement first and random replacement last. By integrating a depreciation expense depending on working time, the cost rate is constructed for each random replacement and some special cases are provided by discussing parameters in the cost rate. Finally, sensitivities to both the proposed warranties and random replacements are analyzed in numerical experiments.
Findings
It is shown that the manufacturer can control the warranty cost by limiting number of random working cycle. For the consumer, when the number of random working cycle is designed as a greater warranty limit, the cost rate can be reduced while the post-warranty period can't be lengthened.
Originality/value
The contribution of this article can be highlighted in two key aspects: (1) the authors investigate early warranties to ensure reliability performance of the product which executes successively projects at random working cycles; (2) by integrating random working cycles into the post-warranty period, the authors is the first to investigate random maintenance policy to sustain the post-warranty reliability from the consumer's perspective, which seldom appears in the existing literature.
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Jiaqi Liu, Jialong Jiang, Mingwei Lin, Hong Chen and Zeshui Xu
When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are…
Abstract
Purpose
When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity. Therefore, the purpose of this paper is to propose an accurate and effective model to predict users’ ratings of products for the accurate recommendation of products to users.
Design/methodology/approach
First, we introduce an attention mechanism that dynamically assigns weights to user preferences, highlighting key interaction information and enhancing the model’s understanding of user behavior. Second, a fold embedding strategy is employed to segment user interaction data, increasing the information density of each subset while reducing the complexity of the attention mechanism. Finally, a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions, thereby improving the model’s generalization ability.
Findings
The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets. On average, the evaluation metrics root mean square error (RMSE) and mean absolute error (MAE) are reduced by 9.11 and 13.3%, respectively. Additionally, the Friedman test results confirm that these improvements are statistically significant. Consequently, the proposed model more accurately captures the intrinsic correlation between users and products, leading to a substantial reduction in prediction error.
Originality/value
We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively. Additionally, we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism. Finally, we implement a masking strategy to encourage the model to focus on key features and patterns, thereby mitigating overfitting.