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As quality becomes increasingly prioritized in supply chain management, understanding how supply chain quality risk management (SCQRM) practices impact quality performance (QP) is essential. This study investigates the effects of two SCQRM practices – risk prevention (RP) and proactive product recall (PPR) – on QP, with a particular focus on the mediating role of supply chain quality integration (SCQI).

A structured survey was administered to gather data from 400 Chinese manufacturing firms. Structural equation modeling was employed to evaluate the proposed relationships among SCQRM practices (RP and PPR), SCQI and QP.

The findings reveal that both RP and PPR significantly and positively influence QP. Specifically, in the structural model, RP exerts a positive effect on SCQI, while PPR also positively impacts SCQI. Additionally, SCQI serves as a mediator between RP and QP, as well as between PPR and QP.

This study contributes to the supply chain management literature by elucidating the beneficial effects of RP and PPR on QP and identifying SCQI as a key mediating factor in these relationships. Leveraging information processing theory (IPT), the study provides new theoretical insights into the mechanisms through which SCQRM enhances QP via SCQI.

Even though trust is known for improving supply chain performance (SCP), previous studies have overlooked the investigation of its dimensions. Limited studies exist on the variations of the influence of trust dimensions in agribusiness supply chain relationships. This study examined the influence of trust dimensions on SCP in a developing country's context.

A cross-sectional study design was used to collect from 204 farmers and 192 traders (396 respondents) using a multistage sampling approach. Structural equation modeling was employed to analyze the hypothesized relationships.

Pooled sample results show that integrity and competence were the trust dimensions with significant effects on SCP, whereas competence was significant across different supply chains and markets, integrity and benevolence were only significant along fresh chains and in the contract market.

The extent of application of this study's findings is limited to situations similar to those of tomato and soybeans value chains in developing countries.

The paper contributes to a better understanding of the influence of trust dimensions on SCP across supply chains in different market typologies in agribusiness relationships in a developing country's context.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.