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The search for reliable tools to assess potential deleterious reactions in sulfide-bearing aggregates, is driven by recent field structure damage involving such types of aggregates. The paper presents a new test scheme with detailed procedures and limit criteria. Five aggregates, including pyrrhotite-bearing reactive ones (Connecticut and MSK 0.9), an alkali-silica reactive low-sulfur limestone (Spratt), a non-reactive control (PKA), and a low-sulfide aggregate (MSK 0.2), were used to manufacture concrete specimens that were subjected to the proposed test. The limit criterion, based on modulus of elasticity loss (RMoE), effectively distinguishes non-reactive, reactive, and potentially reactive aggregates. The scheme's reliability is validated against an existing protocol involving sulfur determination and mortar bar expansion testing. Four of five aggregates exhibit similar potential sulfide reactivity. Despite excessive mortar bar expansion, MSK 0.2, with traces of pyrrhotite, shows non-reactive behavior in concrete specimens after electrochemical treatment. The proposed test scheme offers a practical and timely tool for assessing sulfide-bearing aggregate reactivity before concrete production. [ABSTRACT FROM AUTHOR]

• Using probe data, we estimated the traffic density information on limited access highways. • We used a mixed effect binomial model to link the odds of speeding to traffic density, roadway characteristics, and COVID-19 phases. • We analyzed and compared speeding in two U.S. states, Maine, and Connecticut. • We found that a better level of service such as A, or B (low density) results in higher odds of speeding. • We found that speeding substantially increased during COVID-19 stay-at-home orders and continued to happen even one year after the orders. Stay-at-home orders - imposed to prevent the spread of COVID-19 - drastically changed the way highways operate. Despite lower traffic volumes during these times, the rate of fatal and serious injury crashes increased significantly across the United States due to increased speeding on roads with less traffic congestion and lower levels of speed enforcement. This paper uses a mixed effect binomial regression model to investigate the impact of stay-at-home orders on odds of speeding on urban limited access highway segments in Maine and Connecticut. This paper also establishes a link between traffic density and the odds of speeding. For this purpose, hourly speed and volume probe data were collected on limited access highway segments for the U.S. states of Maine and Connecticut to estimate the traffic density. The traffic density was then combined with the roadway geometric characteristics, speed limit, as well as dummy variables denoting the time of the week, time of the day, COVID-19 phases (before, during and after stay-at-home order), and the interactions between them. Density, represented in the model as Level of Service, was found to be associated with the odds of speeding, with better levels of service such as A, or B (low density) resulting in the higher odds that drivers would speed. We also found that narrower shoulder width could result in lower odds of speeding. Furthermore, we found that during the stay-at-home order, the odds of speeding by more than 10, 15, and 20 mph increased respectively by 54%, 71% and 85% in Connecticut, and by 15%, 36%, and 65% in Maine during evening peak hours. Additionally, one year after the onset of the pandemic, during evening peak hours, the odds of speeding greater than 10, 15, and 20 mph were still 35%, 29%, and 19% greater in Connecticut and 35% 35% and 20% greater in Maine compared to before pandemic. [ABSTRACT FROM AUTHOR]

• Despite reform efforts, the juvenile justice system is permeated with inequities. • Partnering with stakeholders, we developed an integrated model of juvenile justice. • The model layers public health, socioecological and restorative justice frameworks. • Applied examples illustrate how this approach can benefit youth and communities. • Recommendations for engaging in community-driven reform efforts are provided. Approaches to juvenile justice have undergone significant change in recent years, shifting away from retributive models and toward greater focus on rehabilitation and recognition of the unique developmental needs of youth. However, the juvenile justice system is still permeated with significant challenges, including the overrepresentation of vulnerable youth, particularly youth of color, and limited community-based interventions for youth at risk for justice involvement. Current models of juvenile justice do not fully address the needs of at-risk and justice involved youth, and have struggled to effectively reduce racial and ethnic disparities within the system, suggesting there is a need for a model that is better able to conceptualize and respond to youths' needs. The purpose of this paper is to describe the Integrated Model of Juvenile Justice (IMJJ), a novel conceptual approach to juvenile justice reform, which integrates the public health, social-ecological, and restorative justice models. We worked collaboratively with justice system stakeholders in Connecticut to develop the IMJJ, which proposes that juvenile justice practice must take into account and provide for youths' multi-level needs. Through an applied example, this paper describes how the IMJJ can be utilized to target youth offending and promote equitable treatment of youth in the juvenile justice system. Recommendations for justice system stakeholders are provided and implications for policy and practice are discussed. [ABSTRACT FROM AUTHOR]

• A framework for characterizing inundation depth (ID) during compound events is presented. • Relative importance of surge and inland flows in controlling flood ID is quantified. • A new topographic index is introduced to capture the variability of flood ID from river to coast. • With the index areas are identified where compound events are more prominent. Storm surge and river runoff can result in compound flooding in coastal areas. The impact of these events can be more significant than that generated by each component individually. This paper describes a framework for characterizing flooding during compound events and quantifying the relative importance of surge and river flows in controlling inundation depth (ID). For the analysis, we considered 1051 simulations of historical flood events covering about 40 years in Connecticut (CT), USA, and several simulated storms associated with synthetic climate scenarios. We simulated river discharge time series for each event using a physically based distributed hydrological model and retrieved the storm surge from tidal stations. These time series are used as upstream and downstream boundary conditions in 2D hydrodynamic simulation. We focused the analysis on seven locations along the coast of Connecticut covered by LiDAR-derived 1 m DEM. To capture the variability of inundation characteristics over the full-scale gradient from river to coast, we introduced a new topographic index, D-Index, that distinguishes topologic variabilities. Results from this study highlight that there is a correlation of ID to different drivers in distinct categories of the D-Index, which can correctly label the main source of hazard, either coast, river, or compound. We identified thresholds of standard deviation (σ) of the D-Index to identify areas where ID strongly correlates with either flow peak and volume, surge peak, or a combination of both. The study also shows that it is possible to use the results obtained from the 1 m analysis to generalize the findings for the entire CT coast to derive zones dominated by surge, river flow, or the compound effect of the two. This study can help coastal communities better understand the risk of the compound impacts of coastal storms, which is essential for increasing climate resilience. [ABSTRACT FROM AUTHOR]

Due to the variability of working conditions and the scarcity of fault samples, the existing diagnosis models still have a big gap under the condition of covering more practical application scenarios. Therefore, it is of great significance to study an intelligent diagnosis scheme that takes few samples in the training source domain and zero samples in the test target domain (FST-ZST) into account. A Brownian correlation metric prototypical network (BCMPN) algorithm based on a multi-scale mask preprocessing mechanism is proposed for the above problem. First, this paper constructs a multi-scale mask preprocessing mechanism (MMP) to improve the optimization starting point. Second, the multi-scale feature embedding is realized through the dilation convolution module and the effective light channel attention (ELCA) module. Third, based on the Brownian distance similarity measurement, we learn the feature representation by measuring the difference between the joint feature function and the edge product in the field of diagnosis. Finally, based on the gear data set of the Connecticut university (UConn) and the data collected in the laboratory, it is proved that the BCMPN has better performance in the problem of FST-ZST. [ABSTRACT FROM AUTHOR]

Deep learning networks are widely used to realize the intelligent diagnosis of gear faults. However, the problem of the insufficient number of typical fault samples and strong noise often occur in practical applications. Thence, this paper proposes an adaptive anti-noise gear fault diagnosis method based on attention residual prototypical network (ARPN) under the limited sample. In order to maximize the characterization of implicit classification information under fewer samples, frequency slice wavelet transform is applied to convert the vibration signal into a time–frequency image. Then, the Bayesian optimization algorithm is introduced to automatically adjust hyperparameters to meet different application conditions. And the feature embedding stage combined with the improved Non-Local-Pooling-Attention module is constructed to capture the effective feature information better under strong noise conditions. Finally, the internal principle of the proposed model is analyzed based on the visualization process. Meanwhile, the initial application of an interpretable deep learning network in the classification of gear health status has been realized. The ARPN is verified on the Connecticut standard gear data set and the data set collected actually by the laboratory. The results show that the diagnostic accuracy of the ARPN is higher and has stronger recognition ability under different loads. • A novel adaptive-parameter-adjustment gear fault diagnosis method is proposed. • The NLPA module is developed to capture remote dependent features in limited noisy samples. • The improved prototypical network is applied to learn the internal relationships of fewer samples. • The proposed method has superior performance in generalization and robustness. [ABSTRACT FROM AUTHOR]

• A machine learning outage prediction model (OPM) capable of predicting power outages at a circuit (the operational units of the power distribution network) resolution is developed to understand the impact of enhanced vegetation management standards on outages in the electrical distribution grid during storm events. • The authors find good matching between the increase in enhanced vegetation management (ETT) and reduction in trouble spots over the years from 2005 to 2019. • Most of the vegetation management on the electric grid was completed over the last five years of the study (2015–2019), and during those five years the OPM estimates annual reductions of trouble spots between 25.7 and 42.5 percent due to the tree trimming performed. • A novel feature of the OPM developed in this study is the ability to dynamically update it with different vegetation management scenarios at the circuit level and predict the effect of each scenario on trouble spots in the electric grid for storm events. • The authors also find increased prediction accuracy (a reduction in mean absolute percentage error of 4.1% and 5.2% for the two models trained) when including vegetation management data as an input variable to predict trouble spots in the electric grid. This paper develops a machine learning outage prediction model (OPM) to serve as a simulation framework capable of quantifying the reduction in damages to the distribution electric grid due to vegetation management for storm events. The model covers the Eversource Energy distribution grid territory in Connecticut and uses a random forest model with input variables for vegetation, vegetation management, land cover, drought, elevation, weather and electrical infrastructure to predict outages for each circuit (the operational units of the power distribution network). The model is trained on 165 storms from the years 2005 to 2019. The results show that over the last five years of the study (2015–2019) the annual reduction in trouble spots in the electric grid due to enhanced tree trimming is between 25.7 and 42.5% and there is good matching between increased trouble spot reduction and increased vegetation management. Further, we demonstrate improved outage predictions when including vegetation management data as an input variable, with a 4.1% reduction in mean absolute percentage error in leave-one-storm-out cross-validation. This framework could be used to examine varying vegetation management scenarios and the results should be useful for decision makers such as utilities, municipalities and regulators in optimizing vegetation management and broader grid resilience enhancement plans. [ABSTRACT FROM AUTHOR]

Marine hypoxia adversely affects parts of numerous waterbodies around the world. The effects extend to marine organisms. The objective of this paper is twofold. First, we present a theoretical model that can be used to analyze the spatial effects of hypoxia on harvest for any fishing industry. Second, we use temporally and spatially differentiated harvest data for Connecticut Long Island Sound lobster industry to estimate the contemporaneous and lagged effects of hypoxia on harvest across three adjacent fishing zones, only one of which faces moderate to severe hypoxic conditions each summer. The key insight from the theoretical model shows that hypoxia can affect the optimal harvest of both the hypoxic and non-hypoxic fishing zones, and that the impact outside the hypoxic zone will vary across species, depending on how the species responds. Results from the empirical model are consistent with the theoretical findings, i.e., the presence of hypoxic water conditions reduced harvest from both the hypoxic and non-hypoxic water zones during the period between 1995 and 2011. • Analytical framework to explain the spatial effects of hypoxia on harvest. • Model demonstrates behavioral response of fishers in both hypoxic and non-hypoxic fishing zones. • Estimates the contemporaneous and lagged effects of hypoxia on Long Island Sound lobster harvest. [ABSTRACT FROM AUTHOR]