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Urban design professionals face the challenge of creating responsive human settlements amidst complex, ever-evolving interactions between numerous factors, which makes each project a “wicked problem,” requiring designers with specific skills to solve them effectively. This paper explores how integrating “Living Labs” and “Learning Playing techniques” equips urban design students with the needed competencies to address these challenges.

The research employed a literature review focusing on urban design theories related to “wicked problems,” design thinking and soft skills. Through this review, the authors developed the “Urban Design Thinking Soft Skills (UDTS) matrix” and identified pedagogical approaches using Living Labs and Learning Playing Techniques. An award-winning community design studio at Cairo University served as a case study to test and validate the UDTS matrix. Interviews were conducted to further investigate the effectiveness of incorporating these approaches.

The study found that integrating Living Labs principles and Learning Playing Techniques (termed “Joyful Living Labs” in this context) are effective pedagogical methods. Interviews provided evidence supporting their value within urban design curricula for the Egyptian context.

This paper proposes the UDTS matrix as a framework to equip students with the necessary soft skills for tackling complex urban design challenges. Furthermore, it introduces the concept of “Joyful Living Labs” as a potential pedagogical approach specifically suited for the Egyptian context.

Community health workers (CHWs) are vital to addressing public health system limitations in developing countries. However, effective identification and support of underperforming CHWs remains a challenge. This study develops a predictive model to proactively identify underperforming CHWs, facilitating targeted interventions for improved CHW programmes.

We developed a predictive model to identify underperforming CHWs in Uttar Pradesh, India. Data from 140,101 CHWs over a 12-month period was used to build, test and validate the model. Classification techniques, ensemble modeling and a model tuning algorithm were employed for accuracy optimization and early identification.

Logistic regression, decision trees and random forests yielded the best performance. While ensemble models offered no significant performance improvements over the base models, the model tuning algorithm effectively increased prediction accuracy by 19 percentage points. This enabled early identification of poor-performing CHWs and high-risk CHW clusters early in the year.

The developed model has significant potential to improve CHW programmes. It enables targeted support, feedback and resource allocation, leading to enhanced CHW performance, motivation and healthcare outcomes in the communities they serve. The model can provide personalised feedback to help CHWs overcome challenges and dynamic clustering facilitates proactive identification and tailored support for those at risk of underperformance.

This study is the first attempt to use predictive modelling to identify underperforming CHWs, advancing the nascent field of CHW performance analytics. It underscores the effectiveness of digital technologies and data in improving CHW programmes.

This study aims to deal with both the development and mechanical investigations of unsaturated polyester matrix (UPR) composites containing recycled polyethylene terephthalate (PET) fibers as new fillers.

UPR/PET fibers composites have been developed as mats by incorporating 5, 8, 13 and 18 parts per hundred of rubber (phr) of 6-, 10- and 15-mm length PET fibers from the recycling of postconsumer bottles. The mechanical and physical properties of the composites were investigated as a function of fiber content and length. A significant increase in stress at break and in ultimate stress (sr) were observed for composites reinforced with 5 and 8 phr of 15-mm length PET fibers. The Izod impact strength of UPR/mat PET fiber composites as a function of fiber rate and length showed that the 5 and 8 phr composites for the 15-mm length PET fiber have the optimal mechanical properties 13.55 and 10.50 Kj/m², respectively. The morphological study showed that the strong adhesion resulting from the affinity of the PET fiber for the UPR matrix. The ductile fracture of materials reinforced with 5 and 8 phr is confirmed by the fiber deformation and fracture surface roughness.

This study concluded that the PET fiber enhances the properties of composites, a good correlation was observed between the results of the mechanical tests and the structural analysis revealing that for the lower concentrations, the PET fibers are well dispersed into the resin, but entanglements are evidenced when the fiber content increases.

It can be shown from scanning electron microscopy micrographs that the fabrication technique produced composites with good interfacial adhesion between PET fibers and UPR matrix.

With the changing landscape of the globalised business world, business-to-business supply chains face a turbulent ocean of disruptions. Such is the effect that supply chains are disrupted to the point of failure, supply is halted and its adverse effect is seen on the consumer. While previous literature has extensively studied risk and resilience through mathematical modelling, this study aims to envision a novel supply chain model that integrates blockchain to support visibility and recovery resilience strategies.

The stochastic bi-objective (cost and shortage utility) optimisation-based mixed-integer linear programming model integrates blockchain through a binary variable, which activates at a particular threshold risk-averse level of the decision-maker.

Firstly, visibility is improved, as identified by the average reduction of penalties by 36% over the different scenarios. Secondly, the average sum of shortages over different scenarios is consequently reduced by 36% as the recovery of primary suppliers improves. Thirdly, the feeling of shortage unfairness between distributors is significantly reduced by applying blockchain. Fourthly, unreliable direct suppliers resume their supply due to the availability of timely information through blockchain. Lastly, reliance on backup suppliers is reduced as direct suppliers recover conveniently.

The findings indicate that blockchain can enhance visibility and recovery even under high-impact disruption conditions. Furthermore, the study introduces a unique metric for measuring visibility, i.e. penalty costs (lower penalty costs indicate higher visibility and vice versa). The study also improves upon shortages and recoveries reported in prior literature by 6%. Finally, blockchain application caters to the literature on shortage unfairness by significantly reducing the feeling of shortage unfairness among distributors.

This study establishes blockchain as a pro-resilience technology. It advocates that organisations focus on investing in blockchain to enhance their visibility and recovery, as it effectively reduces absolute shortages and feelings of shortage unfairness while improving recovery and visibility.

To the best of the authors’ knowledge, this is a unique supply chain model study that integrates a technology such as blockchain directly as a binary variable in the model constraint equations while also focusing on resilience strategies, costs, risk aversion and shortage unfairness.