This research study presents a resilient control scheme for an islanded DC microgrid (DC MG) integrating solar photovoltaic (PV), battery storage (BESS), and piezoelectric (PE) energy harvesting modules. The microgrid (MG) case study represents an energy hub designed to provide electricity for lighting systems in transportation, roads, and other infrastructure. To enhance practicality, the PE is modeled using the real data captured from a traffic simulator. The proposed reinforcement learning (RL) method was tested against four severe and unexpected failure scenarios, including short circuit at the load side, sudden and severe change of load, open circuit, and converter failure. The performance of the controller was quantitatively compared with a conventional PI controller. The results show marginal improvement in one scenario and significant improvement in the other three, suggesting that the proposed scheme is a robust candidate for microgrids with high levels of uncertainty, such as those involving solar and PE harvesters.

Large-scale construction 3D printing is a promising platform technology that can be leveraged to fabricate structural elements such as columns, piers, pipes, and culverts. In this study, the axial compression and split tensile performance of 3D-printed steel-fiber-reinforced circular elements fabricated with different configurations (hollow, hybrid, mold-cast, and fully 3D-printed) is evaluated. This study further investigates the performance of multi-material circular hybrid elements (3D-printed shells with different backfilled core materials) in an attempt to assess their suitability as a new construction paradigm. The experimental results revealed that the fully 3D-printed steel-fiber-reinforced circular elements exhibited a higher load capacity (up to 36%) and a distinct crack pattern compared to the other configurations. The void ratio of circular elements has varying effects on its axial load capacity depending on the printing material and significantly influences its splitting tensile load capacity. Furthermore, the compatibility between the 3D-printed shell and the cast-in-place core is identified as an influential factor in the structural performance of the hybrid elements. The results suggest a promising construction approach where low-cement material can be utilized as infill material for a cost-effective 3D-printed permanent formwork, offering a viable solution for specific infrastructure development applications.

Planners and policy makers have long been interested in predicting people’s travel behaviors, including the number of vehicle miles traveled (VMT) they generate. Reducing VMT has come to be seen as a key strategy for lowering greenhouse gas emissions and mitigating their associated health effects, as well as for increasing sustainability and equity within communities and on a global scale. Emerging machine learning methods such as boosted regression trees (BRT) allow for the identification of comparative influences of different factors as well as their nonlinear and threshold effects on travel outcomes, but studies comparing the results of these methods with traditional statistical regressions have been scarce. This study is the first to compare these methods’ indications of the impacts of land-use patterns on VMT generation using a large multiregional dataset. The results indicate that the two methods perform similarly in predicting whether households generate any VMT and in predicting the number of VMT generated by those households that generate any. The results of the BRT model validate the statistical models’ indications that built environmental variables contribute significantly to VMT production, while further allowing for the identification of nonlinear impacts. Key thresholds and nonlinear effects of land-use variables on household VMT generation are identified from the BRT model. Both models indicate that land-use patterns that are denser, more diverse, and have increased access to transit result in reductions of vehicular trips and overall VMT, while the BRT model provides effective thresholds for these variables useful for developing planning solutions.

While computer vision has advanced considerably for general object detection and tracking, the specific problem of fast-moving tiny objects remains underexplored. This paper addresses the significant challenge of detecting and tracking rapidly moving small objects using an RGB-D camera. Our novel system combines deep learning-based detection with physics-based tracking to overcome the limitations of existing approaches. Our contributions include: (1) a comprehensive system design for object detection and tracking of fast-moving small objects in 3D space, (2) an innovative physics-based tracking algorithm that integrates kinematics motion equations to handle outliers and missed detections, and (3) an outlier detection and correction module that significantly improves tracking performance in challenging scenarios such as occlusions and rapid direction changes. We evaluated our proposed system on a custom racquetball dataset. Our evaluation shows our system surpassing kalman filter based trackers with up to 70\% less Average Displacement Error. Our system has significant applications for improving robot perception on autonomous platforms and demonstrates the effectiveness of combining physics-based models with deep learning approaches for real-time 3D detection and tracking of challenging small objects.