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Child protective services (CPS) case records contain a vast amount of narrative information that is underutilized for estimating risk, conceptualizing family needs, and planning for services. The current study applied a novel method for quantifying family-level severity of maltreatment and non-maltreatment-related adversity types to narrative information reflecting a family's full CPS history. Cases were randomly sampled (N = 100) from two regions of Connecticut that were referred over a specified 6-month period. De-identified data were extracted through comprehensive chart review of electronic and paper case records. The Yale-Vermont Adversity in Childhood Scale (Y-VACS; Holbrook et al., 2015) was used to quantify adversity severity across a range of intrafamilial and extrafamilial experiences. Several family-level adversity severity ratings were associated with administrative data on allegations and investigative outcomes. Poly-victimization (β =.47, p <.001) and poly-deprivation (β =.25, p =.005) significantly predicted total allegation types and total substantiation types (β =.30, p =.002; β =.26, p =.008, respectively) across the case history. Poly-victimization significantly predicted the presence of a new allegation within 12 months of the index report, OR = 1.72, SE =.25, p =.027. Findings support the feasibility of a novel method that uses narrative case record information to quantify severity of maltreatment and non-maltreatment-related adversity types, as well as cumulative measures of threat- and deprivation-based adversities at the family level. Implications for utilizing case record data to inform CPS intervention are discussed. [ABSTRACT FROM AUTHOR]

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]

• Planning an effective network of fuel stations is a multi-scale location problem. • The method integrates operations research and GIS network analysis techniques. • Optimization models maximize coverage of local, metropolitan, and intercity trips. • GIS tool assesses accessibility of local street intersections from/to freeways. • Scenario planning approach converges on robust set of stations for Hartford CT, USA. Electric drive vehicles (plug-in electric vehicle or hydrogen fuel cell vehicles) have been promoted by governments to foster a more sustainable transportation future. Wider adoption of these vehicles, however, depends on the availability of a convenient and reliable refueling/recharging infrastructure. This paper introduces a path-based, multi-scale, scenario-planning modeling framework for locating a system of alternative-fuel stations. The approach builds on (1) the Flow Refueling Location Model (FRLM), which assumes that drivers stop along their origin-destination routes to refuel, and checks explicitly whether round trips can be completed without running out of fuel, and (2) the Freeway Traffic Capture Method (FTCM), which assesses the degree to which drivers can conveniently reach sites on the local street network near freeway intersections. This paper extends the FTCM to handle cases involving clusters of nearby freeway intersections, which is a limitation of its previous specification. Then, the cluster-based FTCM (CFTCM) is integrated with the FRLM and the DFRLM (FRLM with Deviations) to better conduct detailed geographic optimization of this multi-scale location planning problem. The main contribution of this research is the introduction of a framework that combines multi-scale planning methods to more effectively inform the early development stage of hydrogen refueling infrastructure planning. The proposed multi-scale modeling framework is applied to the Hartford, Connecticut region, which is one of the next areas targeted for fuel-cell vehicle (FCV) market and infrastructure expansion in the United States. This method is generalizable to other regions or other types of fast-fueling alternative fuel vehicles. [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]

• Crash prediction by injury severity and vehicle damage was conducted and compared. • Multivariate Poisson-Lognormal (MVPLN) models were estimated. • Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) models were estimated. • Unobserved heterogeneity was accommodated by both models. • A surrogate measure for injury severity was evaluated. Introduction: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources. Methods: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity when the sample mean of severe injury crashes (e.g., fatal crashes) is very low. Results: The model estimation results highlight the presence of correlations of crash counts among severity levels, as well as the crash counts in total and crash proportions by different severity levels. A comparison of results indicates that injury severity and vehicle damage are highly consistent. Conclusions: Crash severity counts are significantly correlated and should be accommodated in crash prediction models. Practical application: The findings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity. [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]

The purpose of this paper is to describe a best practice model of care for children's mobile crisis services in Connecticut: Emergency Mobile Psychiatric Services (EMPS). EMPS responds to homes, schools, emergency departments and other community locations to provide children and their families with mobile crisis stabilization, assessment and brief intervention, and referral and linkage to ongoing care. The system is comprised of a statewide network of contracted providers, a statewide Call Center to manage and triage incoming referrals, and a Performance Improvement Center to provide data analysis, reporting, quality improvement, and standardized training. Data collected since 2009 demonstrate high service utilization, consistently high mobility rates, and rapid response times as well as statistically significant improvements in child outcomes. The paper discusses the role of mobile crisis services within a comprehensive continuum of behavioral health care for children and families. [ABSTRACT FROM AUTHOR]

Abstract The involvement of consumers in the planning and development of health services has been encouraged and, in some cases, mandated. This involvement is viewed by many as a democratic or ethical requirement in that consumers have a right to influence services. Connecticut has been working toward the full implementation of a children's behavioral health system of care since 1997 but has lacked the mechanisms needed for family members to have their voices heard or to be full partners at decision-making tables. This paper is a case study that highlights work that has been done to 1) gather systematic feedback from families through the use of participatory research methods; 2) increase family access to data; and, 3) build the capacity of caregivers with the goal of increasing their involvement and leadership at all levels of the system of care. Highlights • The involvement of consumers in the planning and development of health services is encouraged. • Involvement of consumers is viewed by many ethical as consumers have a right to influence services they receive. • Connecticut has been working to engage family members as full partners at children's behavioral health decision-making tables. [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]

Abstract: This paper employs syndemics theory to explain high rates of sexually transmitted disease among inner city African American and Puerto Rican heterosexual young adults in Hartford, CT, USA. Syndemic theory helps to elucidate the tendency for multiple co-terminus and interacting epidemics to develop under conditions of health and social disparity. Based on enhanced focus group and in-depth interview data, the paper argues that respondents employed a cultural logic of risk assessment which put them at high risk for STD infection. This cultural logic was shaped by their experiences of growing up in the inner city which included: coming of age in an impoverished family, living in a broken home, experiencing domestic violence, limited expectations of the future, limited exposure to positive role models, lack of expectation of the dependency of others, and fear of intimacy. [Copyright &y& Elsevier]

• 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]

Abstract: Of the many types of barriers to water flow, beaver dams are among the smallest, typically lasting less than a decade and rarely exceeding 1.5m in height. They are also among the most frequent and common obstructions in rivers, with a density often exceeding ten dams per km, a frequency of construction within a given network on a time scale of years, and a historic extent covering most of North America. Past quantification of the geomorphologic impact of beaver dams has primarily been limited to local impacts within individual impoundments and is of limited geographic scope. To assess the impact of beaver dams at larger scales, this study examines channel shape and sediment distribution in thirty river reaches in northeastern Connecticut, U.S.A. The study reaches fall within the broader categories of impounded and free-flowing segments, leaving a third segment class of beaver meadows requiring additional study. Each of the study reaches were classified at the reach scale as free-flowing, valley-wide beaver pond, in-channel beaver pond, and downstream of beaver dam. The bankfull channel width to depth ratios and channel widths normalized by watershed area vary significantly across the study reach classes. Additionally, reaches modified by beaver dams have finer sediment distributions. This paper provides the first quantitative geomorphic descriptions of the in-channel beaver pond and reaches downstream of beaver dams. Given the different channel shapes and sediment distributions, we infer that geomorphic processes are longitudinally decoupled by these frequent barriers that control local base level. These barriers generate heterogeneity within a river network by greatly increasing the range of channel morphology and by generating patches controlled by different processes. Therefore, in spite of the small size of individual beaver dams, the cumulative effect of multiple dams has the potential to modify processes at larger spatial scales. To improve assessment of the larger-scale impacts, we propose a hierarchical classification scheme based on discontinuities, place the reach classes of this study within that scheme, and suggest that further research should continue investigation of discontinuity at the network scale and quantification of the cumulative impacts. [Copyright &y& Elsevier]

Abstract: A public transit performance measure quantifying the ease of reaching a destination from a given location is important for describing the efficiency and convenience of public transit. In this paper, a new method for quantifying public transit performance, the Transit Opportunity Index (TOI), is presented. This measure accounts for both transit accessibility (the level of access to the transit system) and transit connectivity (the system’s provision of services between origins and destinations) by combining measures of spatial coverage, temporal coverage, and trip coverage. Spatial and temporal coverage measures are calculated using an origin–destination (O–D) representation of the transit network and then combined to create a transit accessibility score for each O–D pair. Transit accessibility is weighted by a binary connectivity parameter and a connectivity decay factor. The connectivity decay factor is derived from a travel time-based logistic function to reflect the decreasing connectivity with increasing travel time. The binary connectivity parameter and the connectivity decay factor are used to account for trip coverage, or transit connectivity. The Transit Opportunity Index (TOI) is then applied to the bus network of the city of New Haven, Connecticut. The results of this case study suggest that the TOI is a more complete and practical measure of public transit service performance than previously established measures. This method also has the potential to identify transfer zones for public transit trips between O–D pairs without direct connections. However, the TOI is most powerful when used in conjunction with a public transit demand measure to identify underserved areas. [Copyright &y& Elsevier]

Abstract: This paper introduces dynamic time series modeling in a Bayesian framework to uncover temporal patterns in highway crashes in Connecticut. Existing state sources provide data describing the time for each crash and demographic attributes of persons involved over the time period from January 1995 to December 2009 as well as the traffic volumes and the characteristics of the roads on which these crashes occurred. Induced exposure techniques are used to estimate the exposure for senior and non-senior drivers by road access type (limited access and surface roads) and area type (urban or rural). We show that these dynamic models fit the data better than the usual GLM framework while also permitting discovery of temporal trends in the estimation of parameters, and that computational difficulties arising from Markov Chain Monte Carlo (MCMC) techniques can be handled by the innovative Integrated Nested Laplace Approximations (INLA). Using these techniques we find that while overall safety is increasing over time, the level of safety for senior drivers has remained more stagnant than for non-senior drivers, particularly on rural limited access roads. The greatest opportunity for improvement of safety for senior drivers is on rural surface roads. [Copyright &y& Elsevier]

Abstract: This paper presents a conceptual design of a solar powered hydrogen fueling station for a single family home in Wallingford, Connecticut, USA. Sixty high-efficiency monocrystalline silicon photovoltaic (PV) solar panels (Total capacity: 18.9 kW) account for approximately 94.7% of the hydrogen home’s power consumption. The fueling station consists of a 165 bar high pressure electrolyzer for on-site production of 2.24 kg/day of hydrogen, three-bank cascade configuration storage tanks (4.26 kg of H2 at 350 bar) and a SAE J2600 compliant hydrogen nozzle. The system produces 0.8 kg/day of hydrogen for a fuel cell vehicle with an average commute of 56 km/day (Fuel mileage: 71 km/kg H2). Safety codes and standards applicable at the facility are described, and a well-to-wheel analysis is performed to contrast the carbon dioxide emissions of conventional gasoline and fuel cell vehicles. The energy efficiency obtained by incorporating a solar-hydrogen system for residential applications is also computed. [Copyright &y& Elsevier]

Abstract: Synthetic surfactants are economically important chemicals, as they are widely used in household cleaning detergents, textiles, paints, polymers and personal care products. In this work we have developed a method capable of the isolation and analysis of the most widely used surfactants (linear alkylbenzene sulfonates, LAS, nonylphenol ethoxylates, NPEO, and alcohol ethoxylates, AEO) and their main degradation products (sulfophenyl carboxylic acids, SPC, nonylphenol ethoxycarboxylates, NPEC, and polyethylene glycols, PEG) in aqueous and solid environmental matrices. First, analytes were extracted by ultrasonic extraction from sediments and suspended solids using methanol at 50°C as solvent and 3 cycles (30min per cycle). Clean-up and pre-concentration of the extracts and water samples were carried out by solid-phase extraction (SPE), using Oasis HLB cartridges. Recoveries were generally about 80% for most compounds. Identification and quantification of target compounds were performed by liquid chromatography–time-of-flight-mass spectrometry (LC–ToF-MS), which has been much less used in the field of environmental analysis than other MS techniques. Examples which illustrate the possible advantages of this technique for multi-analyte analysis of target and non-target contaminants in environmental samples are provided. Finally, the methodology developed here was validated by measuring the concentration of surfactants and their metabolites in selected marine sediment and seawater samples collected in Long Island Sound (NY), and in influent and effluent wastewater from Stony Brook treatment plant (NY). This paper presents some of the first data relative to the occurrence of PEG in the environment, especially in sediments where concentrations were generally higher (up to 1490μg/kg) than those for other classes of targeted surfactants and their metabolites. [Copyright &y& Elsevier]

Concerns surrounding possible future climate change, sea level rise, and their potential impacts on coastal environments, have stimulated research seeking to elucidate the relationship between ocean levels and climate at the (sub)century-scale. The need for increasingly precise reconstructions of relative sea-level change has, in-turn, driven the development of new methodologies capable of resolving fine-scale variability within salt-marsh sedimentary sequences. The use of salt-marsh foraminifera as precise indicators of past tide levels has played a central role in this process and is exemplified by a number of detailed studies conducted in Connecticut, USA.In this paper, we apply the most recent methodological advance in the reconstruction of relative sea-levels using salt-marsh foraminifera. We develop a foraminiferal transfer function for tide level, derived from the modern foraminiferal distributions of four Connecticut salt-marshes. In contrast to existing approaches that employ site-specific interpolations of local vertical foraminiferal assemblage zones, this transfer function can provide objective, quantitative and reproducible estimates of palaeomarsh-surface elevation with explicitly stated error terms for marshes throughout Connecticut, irrespective of differences in tidal range.We demonstrate the foraminiferal transfer function approach by employing it to reconstruct changes in palaeomarsh-surface elevation from three cores recovered from Hammock River marsh, Connecticut. The resulting reconstructions, which are precise to ±0.09 m, are tested using an independent dataset from the neighbouring state of Maine, and are used to assess a pivotal record of mean high water (MHW) change from this site. We conclude that the transfer function reconstructions show good general agreement with previous records of palaeomarsh-surface elevation, but with the advantages of quantified error terms and a transparent, reproducible methodology. This replicability will assist in the comparison or combination of local records of MHW change to infer large-scale, regional sea-level variations. [Copyright &y& Elsevier]

Salt-marsh foraminifera are routinely used as sea-level indicators since their vertical distribution is closely linked with elevation relative to the tidal frame. The precise nature of these relationships is variable in time and space, and the accuracy of sea-level reconstructions depends upon the selection of appropriate modern analogues that reliably reflect past fauna–environment associations. The marshes of Connecticut, USA, are sites of ongoing research seeking to produce high-resolution records of sea-level change, yet, little published data regarding their modern foraminiferal distributions exist. This paper presents new surface foraminiferal data from three Connecticut salt-marshes and evaluates their suitability as modern analogues for past sea-level changes. The results indicate that significant intra- and inter-site variability between these marshes and those of neighbouring states exists. As a consequence of this, the extrapolation of fauna–environment relationships developed from marshes with different hydrographic, physiographic, vegetative or climatic characteristics may produce erroneous reconstructions, even when adjusted for variations in tidal range. These errors are potentially greatest if single ‘indicator’ species are used since the relative abundance of individual taxa does not vary consistently with elevation, even in high marsh environments from the same site. Whilst cluster analysis demonstrates that foraminiferal assemblages from Connecticut are vertically zoned with respect to mean high water (MHW), the composition, elevation and height range of these zones is variable between sites. This spatial heterogeneity results in reconstructions of relatively low and variable precision that restricts their utility in high-resolution sea-level research. New studies seeking to distil decimetre-scale changes in relative sea level will need to employ quantitative methods capable of combining multi-site information to develop fauna–environment relationships that capture this spatial variability. Reconstructions will be most precise when employing local foraminiferal distributions, but may require the collection of additional modern analogue samples from other regions when the extant foraminiferal population of a site differs in composition from its sub-fossil counterpart. [Copyright &y& Elsevier]

This paper examines the crisis management response of a Connecticut hospital during an anthrax case that came to be known as the “Oxford Incident.” While crisis communication plans may help organizations effectively manage crises, the findings of this study indicate the established culture at the hospital was the most important factor in the organization’s successful handling of the case. [Copyright &y& Elsevier]