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This work intends to evaluate experimentally the ratcheting behaviour of AM MS300. Furthermore, cyclic plasticity modelling (modified Abdel-Karim and Ohno model) is examined as a means of predicting ratcheting.

Uniaxial stress-controlled cyclic loading histories were utilised to evaluate ratcheting for Maraging Steel 300 (MS300) fabricated via laser powder bed fusion (LPBF) additive manufacturing (AM). Heat-treated and as-built AM and conventionally manufactured (CM) MS300 coupons were tested at room temperature, under constant and incrementally variable stress amplitude and mean stress. Two sets of AM test coupons were used, printed at horizontal and vertical built orientation. The AM material ratcheting was predicted via constitutive modelling and numerical simulation. The Abdel-Karim and Ohno cyclic plasticity model was modified by introducing a memory surface, to improve ratcheting prediction.

The hysteresis stress–strain response and low cycle fatigue (LCF) life were obtained from the different loading histories. Both the AM and CM MS300 exhibited an accumulation of axial strain (ratcheting) for all tests, attributed to the application of non-zero mean stress. The AM MS300 has demonstrated a higher ratcheting accumulation rate than the CM material. The achieved agreement between the numerical results of the new model and the experimental data offers an indication on the suitability and the robustness of this model.

The ratcheting behaviour of the AM MS300 material has been characterised for the first time in the published literature, for a variety of loading histories selected. A modified Abdel-Karim and Ohno plasticity model has been developed to account for the ratcheting performance of this material.

Corruption control in the Republic of the Philippines (RP) has hardly failed, but it is not (yet) a success story either. Dramatic “people power” episodes have occurred, but populism has also, at times, led to repression. Still, essential reform lessons are available.

This is a conceptual reassessment of corruption control. Central concerns include the types of corruption experienced in the country, incentives that might sustain collective action, the need to measure both corruption and reform, and linking reform to citizens’ quality of life.

Top-down, whole-country approaches to reform often treat corruption as if it were the same everywhere, thereby emphasizing “best practices”. However, that can be misleading: a best practice in one society might be irrelevant, impossible, or harmful in another. Contemporary reformers must carefully examine the historical, social, economic, and political contexts, as well as the consequences of corruption. Groups and activities seemingly unrelated to corruption control can prove essential. Reformers must fight corruption with people, not for them.

The ultimate goal of reform must be justice: redressing imbalances of power while building social and political trust.

This paper is a survey of recent academic developments in the literature on green bonds, which have become an important financial instrument in socially responsible investment. This study provides a review of papers that study the market pricing of green bonds, the economic and environmental effects of green bond financing, as well as legal and institutional issues in the green bond market. The literature on market pricing focuses mainly on the existence of greenium, which represents the extent to which green bonds carry a price premium over otherwise identical non-green counterparts. The literature on the economic and environmental effects mainly concerns stock market reaction to green bond issuance and associated economic value implications to other stakeholders, as well as investment in green projects. This paper discusses current issues in the green-bond market and avenues for future research.

This paper aims to provide a driving behavior scoring model to decide the personalized automobile premium for each driver.

Driving behavior scoring model.

The driving behavior scoring model could effectively reflect the risk level of driver’s safe driving.

A driving behavior scoring model for UBI.