The coronavirus pandemic has resulted in major disruptions in the way municipal solid waste management systems (MSWMSs) operate due to substantial distortions in waste generation trends, along with a variety of significant operational and managerial challenges. As critical infrastructure, MSWMSs have endeavored to adapt in response to such unprecedented stresses in order to maintain their operations during the pandemic. The challenges and their relevant adaptive measures, however, have varied with the progression of the pandemic across different MSWMSs. Currently, there is a limited understanding of such time-bound and system-specific phenomena, which impedes timely and effective adaptation. This study aims to fill this knowledge gap by performing a detailed and documented investigation of the longitudinal impact of the coronavirus pandemic on different MSWMSs across the United States, along with its evolution over time, using collected qualitative and quantitative data (i.e., monthly interviews with waste management personnel, online news media, and waste tonnages). This study also develops a relational database system to facilitate the systematic recording and monitoring of the pandemic’s impact on MSWMSs, as well as guide the implementation of different adaptation strategies based on distinct systems’ characteristics. Findings of this study will help solid waste decision-makers better understand the current pandemic, along with serving as a knowledge base for future pandemic scenarios towards more resilient MSWMSs.

Procrastination is one of the critical psychological behaviors affecting labor productivity and can occur throughout a project’s life cycle. Several factors cause this phenomenon, from various personal to environmental factors. Affected by such diverse factors, employees often spend a significant amount of time procrastinating by engaging in nonproductive activities in the workplace, which adds to project costs without adding value. To effectively manage worker procrastination, project managers need to control its root causes throughout the project life cycle. However, the current project management literature lacks a holistic approach to addressing the roots of procrastinationThis study conducts a synthesis review to identify the root causes of workplace procrastination. Then a holistic framework is proposed to assist construction project managers in monitoring and managing procrastination throughout projects. The proposed framework is flexible enough to apply to any project organizational structure and provides several strategies for different phases of project life cycles. The main contribution of this paper is to strengthen our understanding of the procrastination phenomenon (i.e., its root causes and consequences) and recommend preventive practices. The implementation of the proposed life-cycle approach is demonstrated in the context of addressing potential procrastination among foreign construction workers, whose cultural and immigrant background often causes them to procrastinate more.

Infrastructure systems are influenced by a wide range of external factors, including demographic changes, economic cycles, environmental stressors, and technological transitions. These factors affect infrastructure demand, operating conditions, and deterioration processes through nonlinear, delayed, and interacting pathways, causing gradual shifts that can accumulate and emerge as abrupt changes in system performance at higher planning levels. This multiscale behavior creates significant challenges for infrastructure planning, as agencies need to monitor large volumes of heterogeneous data and derive timely, actionable insights from indicators reported at different temporal and spatial scales. Traditional planning methods, which often rely on isolated indicators or static forecasts, struggle to detect emerging patterns and diagnose their underlying drivers across system hierarchies. To address these limitations, this study proposes a hierarchy-aligned composite index framework that integrates diverse external factors and performance measures to support continuous monitoring and adaptive decision-making. The framework combines causal inference methods and dynamic factor analysis to identify influential drivers, extract latent common trends, and organize information in a structure consistent with infrastructure system governance. Rather than merely simplifying interpretation, the composite index streamlines abundant, multidimensional information into interpretable and traceable signals. A case study of Florida’s multimodal transportation system demonstrates the framework’s ability to track and diagnose system behavior across multiple transportation modes and planning levels. Practitioner review with FDOT planners confirms the interpretability and practical relevance of the indices and highlights the framework’s value for adaptive infrastructure planning under uncertainty.