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The study seeks to conduct an empirical investigation on the impact of entrepreneurship education (EE) through its components, i.e. entrepreneurial knowledge (EK) and business planning (BP) on entrepreneurial intentions (EI) in India.

An electronic questionnaire was used to collect data from 340 engineering students and partial least square-structural equation modeling (PLS-SEM) was used to analyze the collected data.

The findings revealed that EK and BP have no direct impact on EI, however, they have an indirect influence through attitude towards entrepreneurship (ATE) and perceived behavioral control (PBC), whereas subjective norms (SN) have no mediation impact on the relationships.

This research has been conducted on students of engineering background only, future studies can be carried out by incorporating more attitudinal and environmental determinants with larger data sizes from diverse educational streams.

This study is of immense significance to policymakers and educational establishments in designing the purposefully designed EE courses that can drive the entrepreneurial intentionality of students.

The study adds to the paucity of research on the systematic elaboration of EE construct underlining the specific impact of EK and BP as EE dimensions on students' EI. To the best of authors' awareness, this kind of investigation has not been conducted in indian higher educational institution (HEI) context.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.