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On retailing platforms, several information‐provision technologies are adopted to gain profit, such as production video ads service, live streaming service, and virtual reality/augmented reality tech. In this article, we focus on the investment strategies of information‐provision tech and its impact on the platform‐based supply chain. To this end, we develop a general model under which the platform invests in information‐provision tech for homogenous sellers and consumers search for products on the platform. Our results show that the platform should adopt a higher investment level in information‐provision tech for the products with the unit search cost or products' information uncertainty degree being medium. Also, a more competitive environment can lead to a lower platform's investment level in information‐provision tech when the number of browsing products is sufficiently large. Interestingly, we find that for a large unit search cost or small uncertainty degree of products' information, investing in information‐provision tech can benefit the platform's and sellers' profit. In addition, if the number of browsing products is large, investing in information‐provision tech can increase the consumer surplus and social welfare. Lastly, our results hold for a broad class of distributions of products' information uncertainty value and other practical cases. Our studies can help the platform to understand the roles of information‐provision tech and provide some practical management insights. [ABSTRACT FROM AUTHOR]

Investing in renewable energy is vital for reducing carbon emissions, yet it also poses significant risks and uncertainties. This study proposes a novel approach to evaluate investment risks in overseas renewable energy projects. First, we apply the Wuli–Shili–Renli (WSR) methodology to develop an evaluation index system for investment risks at three levels—Wuli, Shili, and Renli. Second, a risk assessment framework combining D‐AHP and TOPSIS method is constructed to solve the problem of uncertainty in evaluation information resulting from variations in expert experience. Then we can obtain the risk value. Finally, we apply our approach to evaluate the ADAMA Wind Power Phase I Project in Ethiopia. Our findings reveal a medium risk value for the project, with the highest risk at the Shili level. The management risk is the most significant risk factor, consistent with reality. Our proposed method can effectively address information uncertainty in assessing investment risks of renewable energy projects and is applicable to other similar projects. [ABSTRACT FROM AUTHOR]

Atmospheric state analysis is a difficult scientific problem but essential for atmospheric sciences. Data assimilation can generate accurate analyses by integrating information on the atmospheric state using probability density functions (PDFs), where the Gaussian approximation is typically used and PDFs are described by error covariance matrices (ECMs). However, ECMs have been estimated empirically, and dependency of the ECMs on meteorological conditions (flow) is only partially represented. These limit atmospheric state analysis accuracy. This is especially problematic for water substance‐sensitive microwave radiances (WS‐MWRs) because of their strong flow dependence. We objectively estimated ECMs of all data including flow‐dependence of the ECMs of WS‐MWRs. Since the ECM of each data is a component of one ECM representing one joint PDF as a whole, it is theoretically better to objectively estimate ECMs of all data, not just a particular data. For WS‐MWRs, we categorized flow into four using water substance amount and estimating an ECM for each category. Numerical experiments using the new ECMs on an operational global numerical weather prediction system show the followings. The new error standard deviations are generally smaller than those of empirical. Standard deviations and interchannel correlations of observation errors of WS‐MWRs increase with water substance amount. The effects of WS‐MWRs on analysis were approximately doubled. The analysis fields differ systematically such as increase of low‐level clouds over cold oceans. The forecast accuracy improved with 95% statistical significance up to 9%. Both the flow dependence of correlation and variance of WS‐MWRs contributed to the improvement of forecast accuracy. Plain Language Summary: Atmospheric state analysis is a difficult scientific problem but essential for atmospheric sciences. Data assimilation can generate accurate analysis by integrating information on the atmospheric state using probability density functions typically described by error covariance matrices (ECMs). However, the ECMs include empirical tunings, and their dependency on meteorological conditions (flow) is only partially represented. These limit atmospheric state analysis accuracy. These shortcomings are especially problematic for water substance‐sensitive microwave radiances (WS‐MWRs). We objectively estimate ECMs of all data including flow‐dependence of those of WS‐MWRs. For WS‐MWRs, we categorized flow into four categories by water substance amount and estimate ECMs for each category. Numerical experiments using the estimated objective ECMs (TEST) and conventional empirical ECMs (CNTL) on a global numerical weather prediction system show the followings. The error standard deviations (SDs) of TEST are smaller than those of CNTL for almost all data. For WS‐MWRs, SDs and correlations between different observation wavelengths increase with water substance amount. The effects of WS‐MWRs on analysis were approximately doubled. The analysis fields differ systematically, such as an increase in tropospheric low clouds over cold oceans. The forecast root‐mean‐square errors of TEST are smaller than those of CNTL with 95% statistical significance up to 9%. Key Points: We objectively estimated flow‐dependent error covariance matrices (ECMs) of water substance‐sensitive microwave radiances (WS‐MWRs)The new ECMs doubled effects of WS‐MWRs on analysis, increased low‐level clouds on cold oceans, and improved forecast accuracy up to 9%Both the flow dependence of correlation and variance contributed to the improvement of forecast accuracy [ABSTRACT FROM AUTHOR]

This study focuses on exploring the factors influencing why companies engage in greenhushing decisions in the cultural backgrounds of China and Republic of Korea. Structural equation modeling analysis indicates that in these two countries, reputational risk not only directly leads to corporate greenhushing behavior, but also exerts its influence through conservatism as a mediating factor. Reputational risk causes companies to exhibit conservatism, which in turn leads to the adoption of greenhushing behavior. Furthermore, tests for moderation effects revealed that, in the Chinese sample data, a transparent information disclosure mechanism has the ability to mitigate the impact of reputational risk on corporate greenhushing behavior, as well as to alleviate the influence of conservatism on corporate greenhushing behavior. In the South Korean sample data, a transparent information disclosure mechanism can moderate the impact of reputational risk on conservatism and corporate greenhushing behavior. Finally, the fsQCA method is tested to derive the different configurations and pathways that lead to the strong emergence of greenhushing behavior in companies in China and Republic of Korea, as well as to identify the most influential pathways. This study provides substantial theoretical support for further exploration of the causal mechanisms of corporate greenhushing behavior and promotion of cross‐cultural comparisons and learning in the international environmental management field. In addition, provides rich empirical support and theoretical guidance for corporate environmental management decision‐making and policy formulation. The findings highlight the further standardization and strengthening of transparent information disclosure mechanisms will be an important direction for future environmental policy development. [ABSTRACT FROM AUTHOR]

As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment. [ABSTRACT FROM AUTHOR]

Background: Motor neuron disease (MND) (also known as amyotrophic lateral sclerosis) is a life‐limiting neurodegenerative condition. In up to 20% of people with MND, a pathogenic variant associated with autosomal dominant inheritance can be identified. Children of people carrying a pathogenic variant have a 50% chance of inheriting this and a higher, although harder to predict, chance of developing the disease compared to the general adult population. This paper explores the experience of living with the genetic risk of MND. Methods: We undertook a UK‐based interview study with 35 individuals, including: 7 people living with genetically‐mediated forms of MND; 24 asymptomatic relatives, the majority of whom had an increased risk of developing the disease; and 4 unrelated partners. Results: We explore how individuals make sense of genetic risk, unpacking the interplay between genetic knowledge, personal perception, experiences of the disease in the family, age and life stage and the implications that living with risk has for different aspects of their lives. We balance an emphasis on the emotional and psychological impact described by participants, with a recognition that the salience of risk fluctuates over time. Furthermore, we highlight the diverse strategies and approaches people employ to live well in the face of uncertainty and the complex ways they engage with the possibility of developing symptoms in the future. Finally, we outline the need for open‐ended, tailored support and information provision. Conclusions: Drawing on wider literature on genetic risk, we foreground how knowledge of MND risk can disrupt individuals' taken‐for‐granted assumptions on life and perceptions of the future, but also its contextuality, whereby its relevance becomes more prominent at critical junctures. This research has been used in the development of a public‐facing resource on the healthtalk.org website. Patient or Public Contribution: People with experience of living with genetic risk were involved throughout the design and conduct of the study and advised on aspects including the topic guide, sampling and recruitment and the developing analysis. Two patient and public involvement contributors joined a formal advisory panel. [ABSTRACT FROM AUTHOR]