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This paper aims to design a novel jaw gripper with human-sized anthropomorphic features to be suitable for precise in-hand posture transitions, such as twisting and re-positioning. The growing demand from traditional high-mix low-volume and new massive customized manufacturing industry requires the robot with configurability and flexibility. In the electronic manufacturing industry particularly, the design of the robotic hand with sufficient dexterity and configuration is important for the robot to accomplish the assembly task reliably and robustly. It is important for the robot to be able to grasp and manipulate a large number of assembly parts or tools.

In this research, a novel jaw-like gripper with human-sized anthropomorphic features is designed for online in-hand precise positioning and twisting. It retains the simplicity feature of traditional industrial grippers and dexterity features of dexterous robotic hands.

The gripper is able to apply suitable gripping force on assembly parts and performs reliable twisting movement within limited time to meet the industrial requirements. Manipulating several cylindrical assembly parts by robot, as an experimental case in this paper, is studied to evaluate its performance. The effectiveness of proposed gripper design and mechanical analysis is proved by the simulation and experimental results.

The main originality of this research is that a novel jaw gripper with human-sized anthropomorphic features is designed to be suitable for precise in-hand posture transitions, such as twisting and re-positioning. With this gripper, the robotic system will be sufficiently flexible to deal with various assembly tasks.

This project examines digital modeling strategies for existing buildings. In this context, it aims to question assumptions about the need for geometric accuracy and the efficacy of predefined ontologies. As a counterpoint to prevailing digital modeling strategies, this project proposes a digital modeling approach using a project-specific, emergent ontology.

Nishiki Market, in Kyoto, Japan, is studied as a test case. The emergent-ontology modeling process is introduced with an initial minimal set of operations including basic fold and trim operations applicable to surfaces. As the model develops iteratively, new situations are encountered for which existing rules are insufficient. In response, the model maker’s subjective judgment is invoked to introduce new operations, and ontological rules are allowed to expand.

The emergent-ontology approach, when executed on the Nishiki Market test case, enables representation of specific architectural qualities, highlighting semantic distinctions between digitally modeled elements of real-world features. The modeling approach generated project-specific knowledge, informing disciplinary understanding. Ontological emergence enabled semantic relationships to be disclosed and newly constructed.

The project proposes a novel methodology using an emergent ontology for digitally modeling existing buildings. Instead of remaining within the limitations a predefined ontology, the model maker’s subjective decisions shape the model’s ongoing development. This interpretive approach allows project-specific knowledge generation while challenging prevailing assumptions about accuracy and consistency in digital models of existing buildings.

To determine which proxy institutional quality variables are most significant for a country’s economic growth, the paper aims to investigate the effect of institutional quality on economic growth in upper-middle-income African nations between 2002 and 2021.

In an attempt to account for countries' heterogeneity, the study uses a random and fixed effect model estimated by the generalized least squares.

The empirical findings demonstrate that the institutional quality measures of political stability, voice and accountability, corruption control and violence absence are statistically and favorably significant factors influencing the economic growth of upper-middle-income African countries.

This paper is unique in that it uses both random and fixed effects models to determine the effect of institutional quality on economic growth in the context of upper-middle-income African countries between 2002 and 2021.