Polaroid one-step color photography

Polaroid color photography is based on integral tripack coatings in which each emulsion layer is associated with a dye-developer layer. On development, the chemicals being supplied from a pod, the unused dye developer diffuses to a receptor layer, where the color picture is formed; the oxidized developer is immobilized. A timing layer over an acid polymer layer ensure that pH is high initially for development and then drops. In the integral form (SX-70) opacification shields the light-sensitive layers; the indicator dyes used for this are decolorized by the drop in pH.     

A quantitative risk assessment tool for the external safety of industrial plants with a dust explosion hazard

A quantitative risk assessment (QRA) tool has been developed by TNO for the external safety of industrial plants with a dust explosion hazard. As a first step an industrial plant is divided into groups of modules, defined by their size, shape, and constructional properties. Then the relevant explosion scenarios are determined, together with their frequency of occurrence. These include scenarios in which one module participates, as well as domino scenarios. The frequency is partly based on casuistry.

A typical burning velocity is determined depending on the ignition type, the dust properties and the local conditions for flame acceleration. The resulting pressure development is predicted with the ‘thin flame model’. Module failure occurs when the explosion load exceeds thresholds, which are derived from single degree of freedom (SDOF) calculations for various types of modules. A model has been developed to predict the process of pressure venting after module failure and the related motion of launched module parts.

The blast effects of the primary explosion are based on results from calculations with BLAST3D. The blast and flame effects of the secondary external explosion due to venting are calculated using existing models. The throw of fragments and debris is quantified with a recently developed model. This model is based on trajectory calculations and gives the impact densities, velocities, and angles as output. Furthermore the outflow of bulk material is taken into account. The consequences for external objects and human beings are calculated using existing models. Finally the risk contours and the Societal risk (FN curve) are calculated, which can be compared to regulations.     

Estimating Relevance of Organic Carbon,Nitrogen, and Phosphorus Loads to a Blackwater River Estuary1

Abstract: In blackwater river estuaries, a large portion of external carbon, nitrogen, and phosphorus load are combined in complex organic molecules of varying recalcitrance. Determining their lability is essential to establishing the relationship between anthropogenic loads and eutrophication. A method is proposed in which organic C, N, and P are partitioned into labile and refractory forms, based upon first‐order decay estimated by biochemical oxygen demand relative to total organic carbon, and C:N and C:P ratios as a function of organic carbon lability. The technique was applied in developing total maximum daily loads for the lower St. Johns, a blackwater Atlantic coastal plain river estuary in Northeast Florida. Point source organic nutrients were determined to be largely labile. Urban runoff was found to have the highest relative labile organic N and P content, followed by agricultural runoff. Natural forest and silviculture runoff were high in refractory organic N and P. Upstream labile C, N, and P loads were controlled by autochthonous production, with 34‐50% of summer total labile carbon imported as algal biomass. Differentiation of labile and refractory organic forms suggests that while anthropogenic nutrient enrichment has tripled the total nitrogen load, it has resulted in a 6.7‐fold increase in total labile nitrogen load.     

From manager's emotional intelligence to objective store performance: Through store cohesiveness and sales‐directed employee behavior

The relationships among manager's emotional intelligence, store cohesiveness, sales‐directed employee behavior, and objective store performance were investigated. Non‐managerial sales employees of a large retail electronics chain in South Korea (N = 1611) rated the emotional intelligence of their own store managers as well as the group cohesiveness within their stores. Store managers (N = 253) separately rated the sales‐directed behavior of their employees. Objective sales data were collected one month later for each store. No direct relationship between manager emotional intelligence and objective store performance was found. Instead, the results supported the hypothesized four‐variable, three‐path mediation model: store manager's emotional intelligence was related to store cohesiveness, which in turn was related to the sales‐directed behavior of the frontline employees, which ultimately predicted the objective performance of the stores. Manager emotional intelligence and store cohesiveness are seen as intangible organizing resources or socio‐psychological capital for non‐managerial store employees. Implications for future research and more effective management of retail firms are discussed. Copyright © 2015 John Wiley & Sons, Ltd.     

Circular economy for durable products and materials: the recycling of plastic building products in Germany—status quo,potentials and recommendations

Journal of Material Cycles and Waste Management - The construction sector is the second largest area for the application for plastics. Due to the long life times of construction products, the...     

An ecotoxicological view on neurotoxicity assessment

The numbers of potential neurotoxicants in the environment are raising and pose a great risk for humans and the environment. Currently neurotoxicity assessment is mostly performed to predict and prevent harm to human populations. Despite all the efforts invested in the last years in developing novel in vitro or in silico test systems, in vivo tests with rodents are still the only accepted test for neurotoxicity risk assessment in Europe. Despite an increasing number of reports of species showing altered behaviour, neurotoxicity assessment for species in the environment is not required and therefore mostly not performed. Considering the increasing numbers of environmental contaminants with potential neurotoxic potential, eco-neurotoxicity should be also considered in risk assessment. In order to do so novel test systems are needed that can cope with species differences within ecosystems. In the field, online-biomonitoring systems using behavioural information could be used to detect neurotoxic effects and effect-directed analyses could be applied to identify the neurotoxicants causing the effect. Additionally, toxic pressure calculations in combination with mixture modelling could use environmental chemical monitoring data to predict adverse effects and prioritize pollutants for laboratory testing. Cheminformatics based on computational toxicological data from in vitro and in vivo studies could help to identify potential neurotoxicants. An array of in vitro assays covering different modes of action could be applied to screen compounds for neurotoxicity. The selection of in vitro assays could be guided by AOPs relevant for eco-neurotoxicity. In order to be able to perform risk assessment for eco-neurotoxicity, methods need to focus on the most sensitive species in an ecosystem. A test battery using species from different trophic levels might be the best approach. To implement eco-neurotoxicity assessment into European risk assessment, cheminformatics and in vitro screening tests could be used as first approach to identify eco-neurotoxic pollutants. In a second step, a small species test battery could be applied to assess the risks of ecosystems.     

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.