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This study investigates the factors that influence citizens trust in public leaders [i.e. presidents, members of parliament (MPs) and local government leaders (LGs)] in 34 countries in Africa between 2019 and 2021.

Individual-level data with a sample size of 48,084 was obtained from the Afro-Barometer round 8 survey only and analyzed using multivariate binary logistic regression.

Several important and intriguing observations were made from this analysis: (1) the performance of public leaders influences citizens trust in their leaders; (2) the perceived corruption of public leaders and civil servants and the level of corruption influence citizens trust in public leaders; (3) perceived neighborhood problems (i.e. fear of violence, fear of terrorism and service delivery) influence citizens trust in their public leaders and (4) the socio-demographic characteristics of citizens (i.e. age, religion, education, location, employment and political party affiliation) influence citizens trust in their public leaders.

This study is exceptional in two ways: (1) it examines and compares citizens trust in public leaders across different levels, i.e. presidents, MPs and LGs in Africa and (2) it examines and compares the factors influencing citizens trust in public leaders in Africa comparatively.

The study examines how calculative practices and accountability appear in a rural community of marginalised people in Egypt who depend on jasmine plantations that contribute to the production of global essences.

The data were collected from various sources, namely conversations with villagers, documents and relevant videos and news available on social media and the Internet. This study draws on the concepts of social accountability, the politics of blame avoidance and using calculative practices as a language to explain accountability in context.

The author found a lack of accountability on the part of the government and business owners, with serious implications for the livelihoods of people in a community that has been wholly dependent on jasmine plantations for a century. Power holders have deployed a blame-shifting game to avoid social responsibility. In response, calculative practices rather than advanced accounting tools are used by the poor in the community to induce power holders to be accountable.

The findings of this study show that authorities need to take proactive steps to address the disadvantaged position of powerless people in the lower echelons of society, recognising their accountability for those people.

This paper enhances the understanding of the status of calculative practices and accountability in a community of marginalised people who contribute to the production of global commodities. The paper also enhances the understanding of what goes on behind the scenes with popular and prestigious commodities, whose development is initiated in poor countries, with the end product marketed in rich Western countries.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.