Division of Biological Infrastructure

Purpose Academic burnout among construction management students has become increasingly prominent. This study constructs a theoretical model based on social ecology theory to identify and analyze the multifaceted factors contributing to academic burnout among construction management students in China. The goal was to examine how these factors directly and indirectly affect students' academic performance, mental health, and professional loyalty. Design/methodology/approach A national survey gathered 2,409 responses, and structural equation modeling was employed to evaluate the data. The model assessed the influence of personal, family, school, and social factors on academic burnout by examining both direct and mediated effects. Findings School factors emerged as the most significant contributors to academic burnout, with value education playing the central role. Personal factors, particularly career planning and self-motivation, helped to reduce burnout, whereas family factors had an indirect impact, primarily influencing personal factors. Social factors, while promoting self-worth, also introduced high expectations and pressures, sometimes with adverse effects. Academic burnout was linked to poorer physical and mental health, diminished learning performance, and reduced professional loyalty. Practical implications This study provides actionable recommendations for students, families, schools, and society to alleviate academic burnout and support students' overall development and well-being. Originality/value This research contributes to a comprehensive framework for understanding the direct and indirect influences on academic burnout, offering insights into targeted intervention strategies. Future studies should pursue longitudinal assessments and pilot interventions to validate these findings.

Urban traffic congestion poses a significant global challenge, prompting a shift from unsustainable infrastructure expansion towards optimizing existing networks through intelligent management. This requires robust modeling tools that transcend the limitations of single-link models, which are computationally expensive and fail to capture network-wide phenomena like gridlock. This survey provides a comprehensive review of the Macroscopic Fundamental Diagram (MFD), a pivotal advancement that offers an aggregate description of network traffic flow. We focus specifically on the MFD's characteristics and behavior during the critical transition from uncongested to congested states. The MFD defines a network’s performance through a relationship between vehicle density and flow, forming a curve with a free-flow branch, a critical capacity point, and a congested branch. The manuscript delves into the MFD's theoretical foundations, empirical observations, and modeling approaches. A key insight is that the state transition is complex, exhibiting hysteresis and phase-transition-like behavior, heavily influenced by network heterogeneity and demand patterns. We also address persistent challenges in empirical MFD estimation, including data limitations, while noting how advancements in big data are improving accuracy. Finally, we explore the MFD's practical applications as a foundational tool for developing perimeter control strategies that regulate traffic flow to maintain network efficiency and prevent systemic congestion. This review underscores the MFD's role as a critical paradigm in modern traffic management.

Modern Kubernetes environments underpin mission-critical applications across healthcare, finance, and cloud-native enterprises. While Kubernetes provides robust primitives for container orchestration, it still relies heavily on manual intervention, static rules, and reactive alerts to recover from failures such as pod crashes, node instability, resource exhaustion, and cascading service degradation. As clusters scale in size and complexity, traditional monitoring and rule-based remediation mechanisms become insufficient to meet strict reliability objectives. This paper presents an autonomous Kubernetes cluster healing framework driven by machine learning, designed to proactively detect anomalies, predict failure patterns, and execute self-healing actions without human intervention. The proposed system combines telemetry from Kubernetes control planes, observability platforms, and application-level signals with machine learning models that learn normal and abnormal operational behavior. By integrating predictive analytics with automated remediation workflows, the framework enables clusters to recover from failures faster, reduce mean time to detect (MTTD), and significantly lower mean time to recovery (MTTR). Unlike conventional auto-scaling or threshold-based alerting, the proposed approach leverages historical incident patterns, resource utilization trends, and service-level indicators (SLIs) to make context-aware healing decisions. The architecture supports common remediation actions such as intelligent pod restarts, node cordoning, workload rescheduling, configuration rollbacks, and policy-driven scaling. The framework is cloud-agnostic and applicable to Kubernetes platforms deployed on Azure Kubernetes Service (AKS), Amazon EKS, and hybrid environments. The study demonstrates how machine learning–driven autonomous healing improves cluster resilience, reduces operational toil, and enhances service reliability in regulated, production-grade environments. This work contributes a practical foundation for next-generation self-managing Kubernetes systems and establishes a pathway toward fully autonomous cloud-native infrastructure.

The City of Calgary’s Nose Creek Sanitary Sewer Trunk Phase B project has been constructed over several years to accommodate future population growth and open more developable lands in northern Calgary. The project was divided into three phases, Phase A, B, and C, with each phase delivered by a separate consultant. Phase A was the downstream section of this project and was undertaken by Stantec Consulting. Phase B was the upstream section designed by CH2MHILL (now Jacobs) and further divided into different contracts (1 through 5). Phase C was a separate sewer called the Saddleridge Trunk sewer and this was designed by Associated Engineering. This paper covers Phase B, Contract 4, which had several unique challenges and included many innovative design and procurement solutions. One procurement strategy included a two-phase qualification package, with the initial phase to select a tunnelling contractor with the experience to deliver technical installations under rail and creeks, and the second phase, a request for proposal, to select a general contractor, with broader experience in open-cut and stormwater drainage, which were other aspects included within the project. This approach ensured that The City had the most qualified team available to complete the project. Technical challenges discussed in this paper include an oblique Canadian Pacific Kansas City rail and Nose Creek crossing that required independent review, and computational fluid dynamics and hydrogen sulfide modeling to confirm conditions within the trunk sewer.

This research reports the fabrication of nanocomposite cation exchange membranes by incorporating negatively charged graphene-based nanomaterials into a non-charged poly(vinylidene fluoride) matrix using a mold-casting technique developed in-house.

The relationship between de facto reuse in the Shenandoah River watershed and DBPs in conventional surface water systems in that watershed was examined.

Hysteresis behaviour of metal–insulator transition in VO2 enables reduced energy consumption for high-performance Joule heating driven flexible smart windows.

Novel nanoflower-like N-doped C/CoS2 spheres assembled from 2D wrinkled CoS2 nanosheets were synthesized through a facile one-pot solvothermal method followed by sulfurization. Ascribed to the optimized 3D nanostructure and rational surface engineering, the unique hierarchical structure of the nanoflower-like C/CoS2 composites showed an excellent sodium ion storage capacity accompanied by high specific capacity, superior rate performance and long-term cycling stability. Specifically, the conductive interconnected wrinkled nanosheets create a number of mesoporous structures and thus can greatly release the mechanical stress caused by Na+ insertion/extraction. Besides, it was observed from the experiments that many extra defect vacancies and Na+ storage sites are introduced by the nitrogen doping process. It was also observed that the crosslinked 2D nanosheets can effectively reduce the diffusion lengths of sodium ions and electrons, resulting in an outstanding rate performance (>700 mA h g-1 at 1 A g-1 and 458 mA h g-1 at even 10 A g-1) and extraordinary cycling stability (698 mA h g-1 at 1 A g-1 after 500 cycles). The results provide a facile approach to fabricate promising anode materials for high-performance sodium-ion batteries (SIBs).