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The purpose of this paper was prepared a Ni-based superhydrophobic coating on the surface of copper to enhence its corrosion resistance. The superhydrophobic coating (SHPC) has proven to be an effective surface treatment in corrosion protection. In this paper, a Ni-based SHPC was prepared on the surface of copper (Cu) to enhance its corrosion resistance.

The coating was prepared through a two-step electrodeposition process. The first step involves the formation of a micro-nano structure Ni layer formed by an electrodeposition process. Subsequently, the polysiloxane layer was deposited on the Ni surface to create an SHPC. The morphology, composition, structure, wettability and corrosion resistance of the coating were characterized and discussed.

The results show that the water contact angle of the as-prepared coating reaches 155.5°±1.0°. The corrosion current density (i_corr = 3.90 × 10⁻⁹ A·cm⁻²) decreased by three orders of magnitude compared to the substrate, whereas |Z|_{f = 0.01 Hz} (2.40 × 10⁶ Ω·cm²) increased by three orders of magnitude. It indicated that the prepared coating has excellent superhydrophobicity and high corrosion resistance, which can provide better protection for the substrate.

The prepared coating provides long-lasting protection for Cu and other metals and offers valuable data for developing SHPCs.

This study aims to examine the characteristics of financial analysts’ earnings forecasts after COVID-19 outbroke in the USA. Specifically, the authors examine how financial analysts tradeoff between accuracy and responsiveness under investors’ heightened information demand when there is market-wide uncertainty. In addition, the authors investigate how COVID-19 may affect analysts’ cognitive bias.

The research uses a sample of US-listed firms from March 2019 to February 2021, the period surrounding the COVID-19 outbreak in the USA.

The empirical analyses reveal that analysts issue timelier, more frequent, but less accurate forecasts after the COVID-19 outbreak, indicating that analysts become more responsive to investors’ intensified demand for information during the pandemic. Yet, the high uncertainty caused by COVID-19 increases forecasting difficulty. There is no systematic difference regarding the forecast accuracy between high- and low-ability analysts. Meanwhile, high-quality audit can improve forecast accuracy. Contrary to prior findings that analysts tend to underreact to bad news, the empirical evidence suggests that analysts, shaped by the salience bias, overestimate the negative impact of the pandemic. Analysts first issue pessimistic forecasts at the start of the outbreak and then revise forecasts upward steadily as the fiscal year-end approaches.

The study contributes to the literature by adding novel evidence on how COVID-19-induced uncertainty affects analyst forecast characteristics. It also provides additional evidence on how high-quality audit is associated with improved analyst forecast accuracy even under heightened uncertainty of COVID-19.

With the increasing complexity of public–private partnership (PPP) projects, the amount of data generated during the construction process is massive. This paper aims to develop a new information management method to cope with the risk problems involved in dealing with such data, based on domain ontologies of the construction industry, to help manage PPP risks, share and reuse risk knowledge.

Risk knowledge concepts are acquired and summarized through PPP failure cases and an extensive literature review to establish a domain framework for risk knowledge using ontology technology to help manage PPP risks.

The results indicate that the risk ontology is capable of capturing key concepts and relationships involved in managing PPP risks and can be used to facilitate knowledge reuse and storage beneficial to risk management.

The classes in the risk knowledge ontology model constructed in this research do not yet cover all the information in PPP project risks and need to be further extended. Moreover, only the framework and basic methods needed are developed, while the construction of a working ontology model and the relationship between implicit and explicit knowledge is a complicated process that requires repeated modifications and evaluations before it can be implemented.

The ontology provides a basis for turning PPP risk information into risk knowledge to allow the effective sharing and communication of project risks between different project stakeholders. It can also have the potential to help reduce the dependence on subjectivity by mining, using and storing tacit knowledge in the risk management process.

The apparent suitability of the nine classes of PPP risk knowledge (project model, risk type, risk occurrence stage, risk source, risk consequence, risk likelihood, risk carrier, risk management measures and risk case) is identified, and the proposed construction method and steps for a complete domain ontology for PPP risk management are unique. A combination of criteria- and task-based evaluations is also developed for assessing the PPP risk ontology for the first time.