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A slope digital twin is a virtual slope model that is able to continuously, even in real-time, learn from actual observations (e.g., monitoring data, slope performance records, and site investigation data) obtained from its physical counterpart to enhance the performance of the slope model. This study proposes a practical framework to develop a slope digital twin and describes its application to predict the temporal variation of rainfall-induced slope instability of a real slope in Hong Kong. When compared with a conventional slope model that remains unchanged, the proposed slope digital twin combines monitoring data (e.g., data on rainfall and pore water pressure in the slope) and slope survival records to probabilistically update the model. Specifically, the most suitable model settings are selected, and both the hydraulic and strength parameters of the soils are updated, thereby decreasing the associated uncertainties. The updated slope model can predict pore water pressure re-sponses of a target rainfall consistent with the actual measurements. Furthermore, the model can be used to predict the temporal variation of slope stability (e.g., by using a factor of safety with quantified uncertainty or slope failure probability) during the target rainfall. Because the monitoring data and past slope survival records are incorporated in the model updating, the proposed slope digital twin enhances the prediction of soil hydraulic responses and slope stability. The predicted temporal variation of slope stability agrees well with the observed slope failure induced by an extreme rainstorm in June of 2008.

The concept of zero energy has emerged as the flagship for the achievement of energy conservation and CO2 emissions reduction in the built environment. The implementation of the concept beyond single buildings offers the potential of expanding the scale of zero energy performance while overcoming the limitations of single buildings related to building use, size, on-site renewable energy availability and cost. Literature to date has discussed the zero energy concept at neighborhood scale mostly by looking at theoretical and simulated cases, including both existing and new developments. All types of energy use can be considered for achieving a zero energy balance at neighborhood scale or only the building related component. Often research focuses on Renewable Energy sizing and management for achieving the balance. The present paper analyses the real data obtained from the first year of monitoring of a pilot Zero Energy Neighborhood. A comprehensive monitoring schema, with a Web-GIS monitoring platform at its core, has been developed for the measurement and verification campaign. Performance analysis has shown that the pilot neighborhood has achieved the targets set for the net regulated consumption, renewable energy production, and cost. When considering the total consumption and PV production of the neighborhood, the first five months of monitoring starting from the beginning of summer, it has achieved a positive balance. Overall, the neighborhood has achieved a positive energy balance on a yearly basis for its regulated energy needs

Context. Companies continuously explore their software systems to acquire evidence for software evolution, such as bugs in the system and new functional or quality requirements. So far, managers have made decisions about software evolution based on evidence gathered from interpreting user feedback and monitoring data collected separately from software in use. These evidence-collection processes are usually unmethodical, lack a systematic guide, and have practical issues. This lack of a systematic approach leaves unexploited opportunities for detecting evidence for system evolution. Objective. The main research objective is to improve evidence collection from software in use and guide software practitioners in decision-making about system evolution. Understanding useful approaches to collect user feedback and monitoring data, two important sources of evidence, and combining them are key objectives as well. Method. We proposed a method for gathering evidence from software in use (GESU) using design-science research. We designed the method over three iterations and validated it in the European case studies FI-Start, Supersede, and Wise-IoT. To acquire knowledge for the design, we conducted further research using surveys and systematic mapping methods. Results. The results show that GESU is not only successful in industrial environments but also yields new evidence for software evolution by bringing user feedback and monitoring data together. This combination helps software practitioners improve their understanding of end-user needs and system drawbacks, ultimately supporting continuous requirements elicitation and product evolution. GESU suggests monitoring a software system based on its goals to filter relevant data (i.e., goal-driven monitoring) and gathering user feedback when the system requests feedback about the software in use (i.e., system-triggered user feedback). The system identifies interesting situations of system use and issues automated requests for user

Phytoplankton communities have long been used as water quality indicators within environmental policies. This has fostered the development of national and international phytoplankton monitoring programs, but these networks are subject to sources of uncertainty due to laboratory issues. Nevertheless, studies regarding the interference associated with these aspects are not well-documented. Hence, a long time series (2003-2015) from the Basque continental shelf (southeastern Bay of Biscay) was analyzed to evaluate the uncertainty given by laboratory strategies when studying phytoplankton variability. Variability in phytoplankton communities was explained not only by environmental conditions but also by changes in fixatives (glutaraldehyde and acidic Lugol's solution) and laboratory staff. Based on Bray-Curtis distances, phytoplankton assemblages were found to be significantly dissimilar according to the effect of changes in the specialist handling the sample and the employed fixative. The pair-wise permutational multivariate analysis of variance (PERMANOVA) showed significant differences between the two fixatives utilized and also between the three taxonomists involved. Thus, laboratory-related effects should be considered in the study of phytoplankton time series.

Phytoplankton communities have long been used as water quality indicators within environmental policies. This has fostered the development of national and international phytoplankton monitoring programs, but these networks are subject to sources of uncertainty due to laboratory issues. Nevertheless, studies regarding the interference associated with these aspects are not well-documented. Hence, a long time series (2003-2015) from the Basque continental shelf (southeastern Bay of Biscay) was analyzed to evaluate the uncertainty given by laboratory strategies when studying phytoplankton variability. Variability in phytoplankton communities was explained not only by environmental conditions but also by changes in fixatives (glutaraldehyde and acidic Lugol's solution) and laboratory staff. Based on Bray-Curtis distances, phytoplankton assemblages were found to be significantly dissimilar according to the effect of changes in the specialist handling the sample and the employed fixative. The pair-wise permutational multivariate analysis of variance (PERMANOVA) showed significant differences between the two fixatives utilized and also between the three taxonomists involved. Thus, laboratory-related effects should be considered in the study of phytoplankton time series.

Phytoplankton communities have long been used as water quality indicators within environmental policies. This has fostered the development of national and international phytoplankton monitoring programs, but these networks are subject to sources of uncertainty due to laboratory issues. Nevertheless, studies regarding the interference associated with these aspects are not well-documented. Hence, a long time series (2003-2015) from the Basque continental shelf (southeastern Bay of Biscay) was analyzed to evaluate the uncertainty given by laboratory strategies when studying phytoplankton variability. Variability in phytoplankton communities was explained not only by environmental conditions but also by changes in fixatives (glutaraldehyde and acidic Lugol's solution) and laboratory staff. Based on Bray-Curtis distances, phytoplankton assemblages were found to be significantly dissimilar according to the effect of changes in the specialist handling the sample and the employed fixative. The pair-wise permutational multivariate analysis of variance (PERMANOVA) showed significant differences between the two fixatives utilized and also between the three taxonomists involved. Thus, laboratory-related effects should be considered in the study of phytoplankton time series.

Context. Companies continuously explore their software systems to acquire evidence for software evolution, such as bugs in the system and new functional or quality requirements. So far, managers have made decisions about software evolution based on evidence gathered from interpreting user feedback and monitoring data collected separately from software in use. These evidence-collection processes are usually unmethodical, lack a systematic guide, and have practical issues. This lack of a systematic approach leaves unexploited opportunities for detecting evidence for system evolution. Objective. The main research objective is to improve evidence collection from software in use and guide software practitioners in decision-making about system evolution. Understanding useful approaches to collect user feedback and monitoring data, two important sources of evidence, and combining them are key objectives as well. Method. We proposed a method for gathering evidence from software in use (GESU) using design-science research. We designed the method over three iterations and validated it in the European case studies FI-Start, Supersede, and Wise-IoT. To acquire knowledge for the design, we conducted further research using surveys and systematic mapping methods. Results. The results show that GESU is not only successful in industrial environments but also yields new evidence for software evolution by bringing user feedback and monitoring data together. This combination helps software practitioners improve their understanding of end-user needs and system drawbacks, ultimately supporting continuous requirements elicitation and product evolution. GESU suggests monitoring a software system based on its goals to filter relevant data (i.e., goal-driven monitoring) and gathering user feedback when the system requests feedback about the software in use (i.e., system-triggered user feedback). The system identifies interesting situations of system use and issues automated requests for user

In Italy, the National Environmental Protection Agency (ISPRA) publishes annual reports containing the results of monitoring programs for pesticides in surface and groundwater. These programs were established to accomplish the EU Directives addressing the quality of water resources (drinking water, groundwater and surface water) such as the Water Framework Directive or the Directive on the Sustainable Use of Pesticides. These data can be extremely helpful for risk management actions as they can provide indications where to implement mitigation measures at the territorial scale. In this study, we extrapolated the monitoring data (period 2008–2017) of terbuthylazine and its metabolite desethyl-terbuthylazine in the surface water bodies of the Po plain (Northern Italy). Then, these data were utilized to apply a methodology based on a previous work proposed and published by our research team, which combine, in two distinct phases, historical series of monitoring data and GIS. Overall, the results allowed to identify three areas where to focus risk mitigation measures, which correspond to the low part of the Lombardy (Lodi, Cremona and Mantua area), the area between the Padua and Venice provinces in Veneto and the area of Ferrara in Emilia Romagna.

Context. Companies continuously explore their software systems to acquire evidence for software evolution, such as bugs in the system and new functional or quality requirements. So far, managers have made decisions about software evolution based on evidence gathered from interpreting user feedback and monitoring data collected separately from software in use. These evidence-collection processes are usually unmethodical, lack a systematic guide, and have practical issues. This lack of a systematic approach leaves unexploited opportunities for detecting evidence for system evolution. Objective. The main research objective is to improve evidence collection from software in use and guide software practitioners in decision-making about system evolution. Understanding useful approaches to collect user feedback and monitoring data, two important sources of evidence, and combining them are key objectives as well. Method. We proposed a method for gathering evidence from software in use (GESU) using design-science research. We designed the method over three iterations and validated it in the European case studies FI-Start, Supersede, and Wise-IoT. To acquire knowledge for the design, we conducted further research using surveys and systematic mapping methods. Results. The results show that GESU is not only successful in industrial environments but also yields new evidence for software evolution by bringing user feedback and monitoring data together. This combination helps software practitioners improve their understanding of end-user needs and system drawbacks, ultimately supporting continuous requirements elicitation and product evolution. GESU suggests monitoring a software system based on its goals to filter relevant data (i.e., goal-driven monitoring) and gathering user feedback when the system requests feedback about the software in use (i.e., system-triggered user feedback). The system identifies interesting situations of system use and issues automated requests for user

Context. Companies continuously explore their software systems to acquire evidence for software evolution, such as bugs in the system and new functional or quality requirements. So far, managers have made decisions about software evolution based on evidence gathered from interpreting user feedback and monitoring data collected separately from software in use. These evidence-collection processes are usually unmethodical, lack a systematic guide, and have practical issues. This lack of a systematic approach leaves unexploited opportunities for detecting evidence for system evolution. Objective. The main research objective is to improve evidence collection from software in use and guide software practitioners in decision-making about system evolution. Understanding useful approaches to collect user feedback and monitoring data, two important sources of evidence, and combining them are key objectives as well. Method. We proposed a method for gathering evidence from software in use (GESU) using design-science research. We designed the method over three iterations and validated it in the European case studies FI-Start, Supersede, and Wise-IoT. To acquire knowledge for the design, we conducted further research using surveys and systematic mapping methods. Results. The results show that GESU is not only successful in industrial environments but also yields new evidence for software evolution by bringing user feedback and monitoring data together. This combination helps software practitioners improve their understanding of end-user needs and system drawbacks, ultimately supporting continuous requirements elicitation and product evolution. GESU suggests monitoring a software system based on its goals to filter relevant data (i.e., goal-driven monitoring) and gathering user feedback when the system requests feedback about the software in use (i.e., system-triggered user feedback). The system identifies interesting situations of system use and issues automated requests for user

Context. Companies continuously explore their software systems to acquire evidence for software evolution, such as bugs in the system and new functional or quality requirements. So far, managers have made decisions about software evolution based on evidence gathered from interpreting user feedback and monitoring data collected separately from software in use. These evidence-collection processes are usually unmethodical, lack a systematic guide, and have practical issues. This lack of a systematic approach leaves unexploited opportunities for detecting evidence for system evolution. Objective. The main research objective is to improve evidence collection from software in use and guide software practitioners in decision-making about system evolution. Understanding useful approaches to collect user feedback and monitoring data, two important sources of evidence, and combining them are key objectives as well. Method. We proposed a method for gathering evidence from software in use (GESU) using design-science research. We designed the method over three iterations and validated it in the European case studies FI-Start, Supersede, and Wise-IoT. To acquire knowledge for the design, we conducted further research using surveys and systematic mapping methods. Results. The results show that GESU is not only successful in industrial environments but also yields new evidence for software evolution by bringing user feedback and monitoring data together. This combination helps software practitioners improve their understanding of end-user needs and system drawbacks, ultimately supporting continuous requirements elicitation and product evolution. GESU suggests monitoring a software system based on its goals to filter relevant data (i.e., goal-driven monitoring) and gathering user feedback when the system requests feedback about the software in use (i.e., system-triggered user feedback). The system identifies interesting situations of system use and issues automated requests for user

Objectives: The three centers in this study have different policies regarding cerebral perfusion pressure targets and use of vasopressors in traumatic brain injury patients. The aim was to determine if the different policies affected the estimation of cerebral perfusion pressure which optimizes the strength of cerebral autoregulation, termed "optimal cerebral perfusion pressure." Design: Retrospective analysis of prospectively collected data. Setting: Three neurocritical care units at university hospitals in Cambridge, United Kingdom, Groningen, the Netherlands, and Uppsala, Sweden. Patients: A total of 104 traumatic brain injury patients were included: 35 each from Cambridge and Groningen, and 34 from Uppsala. Interventions: None. Measurements and Main Results: In Groningen, the cerebral perfusion pressure target was greater than or equal to 50 and less than 70mm Hg, in Uppsala greater than or equal to 60, and in Cambridge greater than or equal to 60 or preferably greater than or equal to 70. Despite protocol differences, median cerebral perfusion pressure for each center was above 70mm Hg. Optimal cerebral perfusion pressure was calculated as previously published and implemented in the Intensive Care Monitoring+ software by the Cambridge group, now replicated in the Odin software in Uppsala. Periods with cerebral perfusion pressure above and below optimal cerebral perfusion pressure were analyzed, as were absolute difference between cerebral perfusion pressure and optimal cerebral perfusion pressure and percentage of monitoring time with a valid optimal cerebral perfusion pressure. Uppsala had the highest cerebral perfusion pressure/optimal cerebral perfusion pressure difference. Uppsala patients were older than the other centers, and age is positively correlated with cerebral perfusion pressure/optimal cerebral perfusion pressure difference. Optimal cerebral perfusion pressure was significantly lower in Groningen than in Cambridge. There were no significant diffe

Objectives: The three centers in this study have different policies regarding cerebral perfusion pressure targets and use of vasopressors in traumatic brain injury patients. The aim was to determine if the different policies affected the estimation of cerebral perfusion pressure which optimizes the strength of cerebral autoregulation, termed "optimal cerebral perfusion pressure." Design: Retrospective analysis of prospectively collected data. Setting: Three neurocritical care units at university hospitals in Cambridge, United Kingdom, Groningen, the Netherlands, and Uppsala, Sweden. Patients: A total of 104 traumatic brain injury patients were included: 35 each from Cambridge and Groningen, and 34 from Uppsala. Interventions: None. Measurements and Main Results: In Groningen, the cerebral perfusion pressure target was greater than or equal to 50 and less than 70mm Hg, in Uppsala greater than or equal to 60, and in Cambridge greater than or equal to 60 or preferably greater than or equal to 70. Despite protocol differences, median cerebral perfusion pressure for each center was above 70mm Hg. Optimal cerebral perfusion pressure was calculated as previously published and implemented in the Intensive Care Monitoring+ software by the Cambridge group, now replicated in the Odin software in Uppsala. Periods with cerebral perfusion pressure above and below optimal cerebral perfusion pressure were analyzed, as were absolute difference between cerebral perfusion pressure and optimal cerebral perfusion pressure and percentage of monitoring time with a valid optimal cerebral perfusion pressure. Uppsala had the highest cerebral perfusion pressure/optimal cerebral perfusion pressure difference. Uppsala patients were older than the other centers, and age is positively correlated with cerebral perfusion pressure/optimal cerebral perfusion pressure difference. Optimal cerebral perfusion pressure was significantly lower in Groningen than in Cambridge. There were no significant diffe

This study investigates nutrient-related water quality conditions and change trends in the first management periods of the EU Water Framework Directive (WFD; since 2009) and Baltic Sea Action Plan (BASP; since 2007). With mitigation of nutrients in inland waters and their discharges to the Baltic Sea being a common WFD and BSAP target, we use Sweden as a case study of observable effects, by compiling and analyzing all openly available water and nutrient monitoring data across Sweden since 2003. The data compilation reveals that nutrient monitoring covers only around 1% (down to 0.2% for nutrient loads) of the total number of WFD-classified stream and lake water bodies in Sweden. The data analysis further shows that the hydro-climatically driven water discharge dominates the determination of waterborne loads of both total phosphorus and total nitrogen across Sweden. Both water discharge and the related nutrient loads are in turn well correlated with the ecosystem status classification of Swedish water bodies. Nutrient concentrations do not exhibit such correlation and their changes over the study period are on average small, but concentration increases are found for moderate-to-bad status waters, for which both the WFD and the BSAP have instead targeted concentration decreases. In general, these results indicate insufficient distinction and mitigation of human-driven nutrient components in inland waters and their discharges to the sea by the internationally harmonized applications of the WFD and the BSAP. The results call for further comparative investigations of observable large-scale effects of such regulatory/management frameworks in different parts of the world.

Short-term hazard assessment is an important part of the volcanic management cycle, above all at the onset of an episode of volcanic agitation (unrest). For this reason, one of the main tasks of modern volcanology is to use monitoring data to identify and analyse precursory signals and so determine where and when an eruption might occur. This work follows from Sobradelo and Martí [Short-term volcanic hazard assessment through Bayesian inference: retrospective application to the Pinatubo 1991 volcanic crisis. Journal of Volcanology and Geothermal Research 290, 111, 2015] who defined the principle for a new methodology for conducting short-term hazard assessment in unrest volcanoes. Using the same case study, the eruption on Pinatubo (15 June 1991), this work introduces a new free Python tool, ST-HASSET, for implementing Sobradelo and Martí (2015) methodology in the time evolution of unrest indicators in the volcanic short-term hazard assessment. Moreover, this tool is designed for complementing long-term hazard assessment with continuous monitoring data when the volcano goes into unrest. It is based on Bayesian inference and transforms different pre-eruptive monitoring parameters into a common probabilistic scale for comparison among unrest episodes from the same volcano or from similar ones. This allows identifying common pre-eruptive behaviours and patterns. ST-HASSET is especially designed to assist experts and decision makers as a crisis unfolds, and allows detecting sudden changes in the activity of a volcano. Therefore, it makes an important contribution to the analysis and interpretation of relevant data for understanding the evolution of volcanic unrest. © 2016 Elsevier Ltd.

By integrating agri-environmental databases, mathematical models and geographic information systems, maps showing the potential vulnerability of soils to the leaching of plant protection products can be generated. However, these forecasts may not be subsequently corroborated by monitoring data. Here we present a case study based on glyphosate in Lombardy (Italy) and triazine herbicides in the Autonomous Community of Valencia (Spain). Glyphosate was found in the groundwater of Lombardy, despite modeling results clearly indicating the non-potential risk of groundwater contamination. Among the triazine herbicides in Valencia, simazine, although present in surface waters, was not found in groundwaters, contrary to its expected behavior as a potential leachate. The discrepancy in the behavior of glyphosate can be explained by infiltration and point contamination sources, and the absence of simazine by the facility of its degradation. Our study highlights the importance of integrating modeling and mapping approaches to improve knowledge and to obtain quality data. [Contrib Sci 10:151-160 (2014)]

© 2014 . One of the most challenging aspects of managing a volcanic crisis is the interpretation of the monitoring data, so as to anticipate to the evolution of the unrest and implement timely mitigation actions. An unrest episode may include different stages or time intervals of increasing activity that may or may not precede a volcanic eruption, depending on the causes of the unrest (magmatic, geothermal or tectonic). Therefore, one of the main goals in monitoring volcanic unrest is to forecast whether or not such increase of activity will end up with an eruption, and if this is the case, how, when, and where this eruption will take place. As an alternative method to expert elicitation for assessing and merging monitoring data and relevant past information, we present a probabilistic method to transform precursory activity into the probability of experiencing a significant variation by the next time interval (i.e. the next step in the unrest), given its preceding evolution, and by further estimating the probability of the occurrence of a particular eruptive scenario combining monitoring and past data. With the 1991 Pinatubo volcanic crisis as a reference, we have developed such a method to assess short-term volcanic hazard using Bayesian inference.

By integrating agri-environmental databases, mathematical models and geographic information systems, maps showing the potential vulnerability of soils to the leaching of plant protection products can be generated. However, these forecasts may not be subsequently corroborated by monitoring data. Here we present a case study based on glyphosate in Lombardy (Italy) and triazine herbicides in the Autonomous Community of Valencia (Spain). Glyphosate was found in the groundwater of Lombardy, despite modeling results clearly indicating the non-potential risk of groundwater contamination. Among the triazine herbicides in Valencia, simazine, although present in surface waters, was not found in groundwaters, contrary to its expected behavior as a potential leachate. The discrepancy in the behavior of glyphosate can be explained by infiltration and point contamination sources, and the absence of simazine by the facility of its degradation. Our study highlights the importance of integrating modeling and mapping approaches to improve knowledge and to obtain quality data. [Contrib Sci 10:151-160 (2014)]

A review of the available research indicates that relatively little is known about how districts employ Academic Achievement Teams or similar mechanisms to reduce declines in student achievement and sustain increased student achievement at the elementary school level (Kutash, Nico, Gorin, Rahmatullah, & Tallant, 2010). Turning around chronically low-performing schools is challenging work requiring a systemic rather than school-by-school approach (Robinson & Buntrock, 2011). The most successful turnaround efforts have both high-impact leaders and the district capacity to initiate, support, and enhance transformational change through the use of data. Educational leaders on all levels are realizing meaningful information can only be acquired through a proper analysis of data and good decisions are based on this thoughtful process of inquiry and analysis (Creighton, 2007). The intent of this study was to identify practices of Academic Achievement Teams that facilitated student achievement. Interviews were conducted with principals, directors of elementary education, a teacher, and district liaison representing the Virginia Department of Education\'s Office of School Improvement to gain insight into the operational and organizational structures of the Academic Achievement Teams. A qualitative design was selected to conduct this descriptive cross case study. In addition to the one-on-one interviews, observations of the Academic Achievement Team meetings and review of documents from each of the two study schools were examined to gain additional perspective regarding how the Academic Achievement Team operated to increase student achievement. The interviews, observations, and document reviews were analyzed using the Constant Comparative Method to understand the specific practices employed by Academic Achievement Teams that increased student achievement at two elementary schools (Maykut & Morehouse, 1994).

Adaptive harvest management (AHM) can assist biologists with decisions made under uncertainty. There have been few applications of AHM to manage wildlife at the state level, and we provide a theoretical exercise using AHM in the context of Greater Prairie-Chicken harvest in southeast Nebraska. Our goals were to develop and evaluate an AHM framework for a state specific harvest decision, and to use the AHM process to evaluate uncertainties associated with harvest mortality for Greater Prairie-Chickens in Nebraska. Harvest of prairie chickens in southeast Nebraska was restarted 2000, using a special limited permit system, and was controversial with respect to the potential impacts of harvest on a recovering population. We followed standard steps to develop our AHM framework and created a formal utility function to reward harvest regulations that would meet management objectives. We used observed spring counts of males at leks and predicted counts from two competing alternative models based on additive and compensatory harvest mortality to weight our confidence in each model. Our AHM framework provided a framework to select the optimal harvest regulation package. Harvest rates averaged 0.057 as a proportion of the fall population during 2000–2007, and count data suggested that the population was relatively stable. The compensatory harvest mortality model had achieved >99% confidence by 2004, which suggests that harvest mortality in this population may be compensatory for harvest rates <0.06. Our exercise shows that AHM can be effectively applied to harvest decisions at a small geographic scale, and we encourage biologists to consider using data on harvest to formally gain information that will enhance harvest management.

There are many examples in which proportional hazards modelling (PHM) is used to accurately model the effects of the operating environment on an item's lifetime. Using such techniques it is possible to find an economically optimal moment of replacement based on condition monitoring data. However, in a nuclear power plant, the problem is somewhat different as it is not possible to stop the process and perform preventive maintenance at the most economical moment in time. The problem of interest in such cases has more to do with whether the item will last until the next scheduled plant shutdown or not. This paper presents some developed and implemented theory to calculate the conditional reliability function of an item given the current equipment age and the current values of the condition-monitoring variables. A case study of a shaft seal for a reactor cooling pump (RCP) using data from several French nuclear power plants operated by the Electricité de France (EDF) is also presented., QC 20141119