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Abstract In cold regions, the environment dynamics lead to variations of soil temperature, water content, and deformation, which are characterized by highly coupled physical interplay. The hydraulic and thermal properties of unsaturated soils are highly nonlinear, which is further complicated when subjected to freezing. This paper presents a comprehensive multiphysics coupling model to evaluate these complex processes. The model considers the behaviors of unsaturated frozen soils. It accounts for the influences of meteorological, geothermal, and hydrological factors. The model is validated through two pavement case studies using Long-Term Pavement Performance (LTPP) road section data. The first case analysis is performed for a pavement section in Vermont, and the simulation lasted for 30 days during a non-freezing season on an hourly basis. The results validated the performance of the model considering unsaturated soil behaviors. The second case study is based on a daily analysis of a pavement section in South Dakota over a freezing–thawing cycle over 194 days. The results validated the model in considering the frozen unsaturated soil behaviors. Both case studies demonstrate the performance of this comprehensive model in quantifying the spatial and temporal variations of soil temperature and water content in response to environmental stressors. The capability of the model in accurately predicting the responses of pavement to the meteorological factors unleashes the potential of this model to assess the effects of climate and climate change on cold region pavement, as well as other types of geo-structures.

Abstract There are over 2.2 million miles of underground water pipes serving the cities in the United States. Many are in poor conditions and deteriorate rapidly. Failures of these pipes could cause enormous financial losses to the customers and communities. Inspection provides crucial information for pipe condition assessment and maintenance plan; it, however, is very expensive for underground pipes due to accessibility issues. Therefore, water agencies commonly face the challenge to 1) decide whether it is worthwhile to schedule expensive water pipe inspections under financial constraints, and 2) if so, how to optimize the inspection schedule to maximize its value. This study leverages the physical model and data-based ML (ML) models for underground water pipe failure prediction to shed light on these two important questions for decision making. Analyses are firstly conducted to assess the value of water pipe inspection. Results by use of a physical-based failure model and Monte Carlo simulations indicate that by inspecting pipe’s condition, i.e., assessment of pipe’s erosion depth, the uncertainty of water pipe failure prediction can be narrowed down by 51%. For optimal inspection schedule, an artificial neural network (ANN) model, trained with historical inspection data, is evaluated for its performance in forecasting the future pipe failure probability. The results showed that a biased pipe failure prediction can occur under limited rounds of inspection. However, incorporating more rounds of inspection allows to predict the pipe failure conditions over its life cycle. From this, an optimal inspection plan can be proposed to achieve the maximum benefits of inspection in uncertainty reduction. A few salient results from the analyses include 1) the optimal schedule for inspection is not necessarily equal in the time interval, 2) by setting the goal of uncertainty reduction, an optimal inspection schedule can be obtained, where ML (ML) model augmented by continuously training with inspection data allows to reliably predict water pipe failure conditions over its life cycle. While this study focuses on underground pipe inspection, the general observations and methodology are applicable to optimize the inspection of other types of infrastructure as well.

This research examines whether prior research may not have detected cultural-specificity in the role of controlling and autonomy-supportive parenting in children's adjustment because of reliance on between-individual analyses. In two longitudinal studies (Ns = 825 and 934) of early adolescents, within-individual analyses were conducted to examine the reciprocal pathways between children's reports of parenting and their reports of their adjustment in the United States and China. Increments in controlling parenting predicted decrements in children's emotional and behavioral, but generally not academic, adjustment over time, with little evidence that this was stronger in the United States than in China. Decrements in children's emotional and behavioral, but generally not academic, adjustment predicted increments in controlling parenting over time similarly in the two countries. There were few overtime pathways involving autonomy-supportive parenting in either direction in either country.

Abstract Purpose To explore the outcome and prognostic factors between inv(16) and t(8;21) disrupt core binding factor (CBF) in acute myeloid leukemia (AML). Methods The clinical characteristic, probability of achieving complete remission (CR), overall survival (OS) and cumulative incidence of relapse (CIR) were compared between inv(16) and (8;21). Results The CR rate was 95.2%, 10-year OS was 84.4% and CIR was 29.4%. Subgroup analysis showed that patients with t(8;21) had significant lower 10-year OS and CIR than patients with inv(16). Unexpectedly, there was a trend for pediatric AML receiving five courses cytarabine to have a lower CIR than four courses cytarabine (19.8% vs 29.3%, P = 0.06). Among the cohort of no-gemtuzumab ozogamicin(GO) treatment, inv (16) patients showed a similar 10-year OS (78.9% vs 83.5%; P = 0.69) and an inferior outcome on 10-year CIR (58.6% vs 28.9%, P = 0.01) than those patients with t(8;21). In contrast, inv (16) and t(8;21) patients receiving GO treatment had comparable OS (OS: 90.5% vs. 86.5%, P = 0.66) as well as CIR (40.4% vs. 21.4%, P = 0.13). Conclusion Our data demonstrated that more cumulative cytarabine exposure could improve the outcome of childhood patients with t(8;21), while GO treatment was beneficial to the pediatric patients with inv(16).

Abstract Climate change is projected to have profound impacts on the resilience and sustainability of built infrastructure. This study aims to understand the impacts of climate change on water supply systems and to facilitate adaptive actions. A premium database maintained by the Cleveland Water Division, Cleveland, Ohio, USA is analyzed. It contains 29,621 pipe failure records of 51,832 pipes over the past 30 years, representing one of the largest dataset in current literature. From the database, pipe failure rate models have been developed for water pipes made of different types of materials at different ages. The influence of climate (temperature and precipitation) on fragility of water pipes are obtained. Based on the developed climate-fragility failure rate models, the impacts of climate change on the water systems located in different geographic regions are evaluated by predicting the failure rate and number of failures in the water systems in the next 80 years (2020 to 2100). Climate models are used to predict weather under different climate change scenarios. The results demonstrate that the impacts of climate change on water supply system are likely complicated and are dependent upon factors such as the geographic location, pipe material, pipe age, and maintenance strategies. Water pipes in the cold regions may experience fewer number breaks due to the warmer weather and less severe winter, whereas those located in the hot regions may experience more failures associated with more corrosion. Different pipe replacement strategies are compared, which demonstrate the importance of considering the aging of water supply system in future maintenance decisions. This study enriches current understandings on the impacts of climate change on the water systems. The results will help water utilities to design climate change adaptation strategies.