Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena

Corporate brand management imperatives: Custodianship, credibility, and calibration

Copyright 2012 by The Regents of the University of California. All rights reserved.Marshaling case study research insights, this article advances our knowledge of the strategic management of corporate brands. Strategic corporate brand management requires commitment to three critically important imperatives: senior management custodianship; the building and maintaining of brand credibility; and the dynamic calibration of seven identities constituting the corporate brand constellation. This article draws on research dating back to the 1990s and is also informed by the identity-based view of corporate brands perspective and by recent scholarship on the AC4ID Test—a strategic, diagnostic, corporate brand management framework

Active reservoir management: a model solution

Steady-state procedures, of their very nature, cannot deal with dynamic situations. Statistical models require extensive calibration, and predictions often have to be made for environmental conditions which are often outside the original calibration conditions. In addition, the calibration requirement makes them difficult to transfer to other lakes. To date, no computer programs have been developed which will successfully predict changes in species of algae. The obvious solution to these limitations is to apply our limnological knowledge to the problem and develop functional models, so reducing the requirement for such rigorous calibration. Reynolds has proposed a model, based on fundamental principles of algal response to environmental events, which has successfully recreated the maximum observed biomass, the timing of events and a fair simulation of the species succession in several lakes. A forerunner of this model was developed jointly with Welsh Water under contract to Messrs. Wallace Evans and Partners, for use in the Cardiff Bay Barrage study. In this paper the authors test a much developed form of this original model against a more complex data-set and, using a simple example, show how it can be applied as an aid in the choice of management strategy for the reduction of problems caused by eutrophication. Some further developments of the model are indicated

Tracing and cataloguing knowledge in an e-health cardiology environment

AbstractIn an e-health cardiology environment, the current knowledge engineering systems can support two knowledge processes; the knowledge tracing, and the knowledge cataloguing.We have developed an n-tier system capable of supporting these processes by enabling human collaboration in each phase along with, a prototype scalable knowledge engineering tactic. A knowledge graph is used as a dynamic information structure. Biosignal data (values of HR, QRS, and ST variables) from 86 patients were used; two general practitioners defined and updated the patients’ clinical management protocols; and feedback was inserted retrospectively. Several calibration tests were also performed.The system succeeded in formulating three knowledge catalogues per patient, namely, the “patient in life”, the “patient in time”, and the “patient in action”.For each patient the clinically accepted normal limits of each variable were predicted with an accuracy of approximately 95%. The patients’ risk-levels were identified accurately, and in turn, the errors were reduced. The data and the expert-oriented feedback were also time-stamped correctly and synchronized under a common time-framework.Knowledge processes optimization necessitates human collaboration and scalable knowledge engineering tactics. Experts should be responsible for resenting or rejecting a process if it downgrades the provided healthcare quality

Design of sediment oxygen demand (SOD) 'in-situ' measuring chamber and its application in several rivers

Sediment oxygen demand is defined as the rate of dissolved oxygen removal from the water column by the decomposition of organic materials in the bottom sediments. Accurate SOD rates are important, as they will allow for more precise permits specifications and therefore the degree or level of wastewater treatment needed. The "in situ" SOD chamber designed for the study was adapted from an earlier design by the USEPA (Hatcher, 1986). Two sets of chambers of differing sizes were fabricated and used to measure the SOD levels in several rivers and a small lake. These measurements could be used for the calibration and validation water quality models. Another applied usage of the SOD chamber was for the management of aquaculture ponds. The knowledge of the SOD levels at the bottom of the aquaculture ponds will allow for a more systematic pond-cleaning schedule

Assessing spatiotemporal correlations from data for short-term traffic prediction using multi-task learning

Traffic flow prediction is a fundamental problem for efficient transportation control and management. However, most current data-driven traffic prediction work found in the literature have focused on predicting traffic from an individual task perspective, and have not fully leveraged the implicit knowledge present in a road-network through space and time correlations. Such correlations are now far easier to isolate due to the recent profusion of traffic data sources and more specifically their wide geographic spread. In this paper, we take a multi-task learning (MTL) approach whose fundamental aim is to improve the generalization performance by leveraging the domain-specific information contained in related tasks that are jointly learned. In addition, another common factor found in the literature is that a historical dataset is used for the calibration and the assessment of the proposed approach, without dealing in any explicit or implicit way with the frequent challenges found in real-time prediction. In contrast, we adopt a different approach which faces this problem from a point of view of streams of data, and thus the learning procedure is undertaken online, giving greater importance to the most recent data, making data-driven decisions online, and undoing decisions which are no longer optimal. In the experiments presented we achieve a more compact and consistent knowledge in the form of rules automatically extracted from data, while maintaining or even improving, in some cases, the performance over single-task learning (STL).Peer ReviewedPostprint (published version

Knowledge-based assistance in costing the space station DMS

The Software Cost Engineering (SCE) methodology developed over the last two decades at IBM Systems Integration Division (SID) in Houston is utilized to cost the NASA Space Station Data Management System (DMS). An ongoing project to capture this methodology, which is built on a foundation of experiences and lessons learned, has resulted in the development of an internal-use-only, PC-based prototype that integrates algorithmic tools with knowledge-based decision support assistants. This prototype Software Cost Engineering Automation Tool (SCEAT) is being employed to assist in the DMS costing exercises. At the same time, DMS costing serves as a forcing function and provides a platform for the continuing, iterative development, calibration, and validation and verification of SCEAT. The data that forms the cost engineering database is derived from more than 15 years of development of NASA Space Shuttle software, ranging from low criticality, low complexity support tools to highly complex and highly critical onboard software

Small farmers' perception of factors influencing regional chemical control of Diaphorina citri

Regional Control Areas (RCAs) have been implemented in Mexico as a strategy to delay the spread of Candidatus Liberibacter asiaticus, the causal bacterium of the disease known as Huanglongbing (HLB). The implementation of an effective management of the vector insect, Diaphorina citri in the RCAs requires the knowledge, acceptance and coordinated engagement of small agricultural producers. This research assessed the perception and knowledge of 62 citrus growers regarding the operational, sociocultural and environmental factors influencing chemical control of D. citri in four RCAs within Veracruz State. According to their responses, the following factors have been identified as the operational factors with the highest influence on the effectiveness of insecticides against D. citri within RCAs: the lack of knowledge about the use of surfactants, application speed, poor calibration of sprayers and incorrect water quality. The most significant sociocultural factors are the general unawareness of the pest and the safe and proper application of pesticides. The most relevant environmental factors during application: temperature, relative humidity, and wind speed. Sociocultural index correlated with the perception of effectiveness. Therefore, it becomes necessary to consider differences among citrus growers in each region and setting out the most appropriatestrategies for vector and disease management. Highlights Some operational practices that citrus growers are not aware of may influence their perception of chemical control. The sociocultural context of growers determines their decision-taking on insecticide applications. During the application of insecticides in regional control areas (RCAs), growers do not take into account weather conditions. The effective management of D. citri requires a coordinated engagement of small growers' in RCAs.Regional Control Areas (RCAs) have been implemented in Mexico as a strategy to delay the spread of Candidatus Liberibacter asiaticus, the causal bacterium of the disease known as Huanglongbing (HLB). The implementation of an effective management of the vector insect, Diaphorina citri in the RCAs requires the knowledge, acceptance and coordinated engagement of small agricultural producers. This research assessed the perception and knowledge of 62 citrus growers regarding the operational, sociocultural and environmental factors influencing chemical control of D. citri in four RCAs within Veracruz State. According to their responses, the following factors have been identified as the operational factors with the highest influence on the effectiveness of insecticides against D. citri within RCAs: the lack of knowledge about the use of surfactants, application speed, poor calibration of sprayers and incorrect water quality. The most significant sociocultural factors are the general unawareness of the pest and the safe and proper application of pesticides. The most relevant environmental factors during application: temperature, relative humidity, and wind speed. Sociocultural index correlated with the perception of effectiveness. Therefore, it becomes necessary to consider differences among citrus growers in each region and setting out the most appropriatestrategies for vector and disease management. Highlights Some operational practices that citrus growers are not aware of may influence their perception of chemical control. The sociocultural context of growers determines their decision-taking on insecticide applications. During the application of insecticides in regional control areas (RCAs), growers do not take into account weather conditions. The effective management of D. citri requires a coordinated engagement of small growers' in RCAs

Using the soil and water assessment tool to simulate the pesticide dynamics in the data scarce Guayas River Basin, Ecuador

Agricultural intensification has stimulated the economy in the Guayas River basin in Ecuador, but also affected several ecosystems. The increased use of pesticides poses a serious threat to the freshwater ecosystem, which urgently calls for an improved knowledge about the impact of pesticide practices in this study area. Several studies have shown that models can be appropriate tools to simulate pesticide dynamics in order to obtain this knowledge. This study tested the suitability of the Soil and Water Assessment Tool (SWAT) to simulate the dynamics of two different pesticides in the data scarce Guayas River basin. First, we set up, calibrated and validated the model using the streamflow data. Subsequently, we set up the model for the simulation of the selected pesticides (i.e., pendimethalin and fenpropimorph). While the hydrology was represented soundly by the model considering the data scare conditions, the simulation of the pesticides should be taken with care due to uncertainties behind essential drivers, e.g., application rates. Among the insights obtained from the pesticide simulations are the identification of critical zones for prioritisation, the dominant areas of pesticide sources and the impact of the different land uses. SWAT has been evaluated to be a suitable tool to investigate the impact of pesticide use under data scarcity in the Guayas River basin. The strengths of SWAT are its semi-distributed structure, availability of extensive online documentation, internal pesticide databases and user support while the limitations are high data requirements, time-intensive model development and challenging streamflow calibration. The results can also be helpful to design future water quality monitoring strategies. However, for future studies, we highly recommend extended monitoring of pesticide concentrations and sediment loads. Moreover, to substantially improve the model performance, the availability of better input data is needed such as higher resolution soil maps, more accurate pesticide application rate and actual land management programs. Provided that key suggestions for further improvement are considered, the model is valuable for applications in river ecosystem management of the Guayas River basin

The influence of the calibration metric on design flood estimation using continuous simulation

© 2016 International Association for Hydro-Environment Engineering and Research. Estimation of design flood flow has been and remains a concern for both hydrologic research and hydrologic practice. Knowledge of design flood flows provides a basis for sustainable flood management, which has the aim of reducing flood risk, thereby protecting people’s lives and property. Design floods for a given location can be estimated by a number of approaches including analysis of past flood statistics and the use of catchment modelling. When catchment modelling approaches are applied estimation of design flood flows, there is a need to calibrate the model parameters. As part of this calibration process, a calibration metric, or fitness measure, is needed to enable assessment of alternative sets of parameter values. Presented herein is an investigation into design flood quantiles derived from predictions obtained from a continuous catchment modelling system when alternative calibration metrics are used to assess the suitability of parameter values. Two alternative calibration metrics are considered with one calibration metric aimed at ensuring replication of recorded hydrographs and the second calibration metric aimed at ensuring replication of the statistical characteristics of the annual maxima series. It was found that use of the later calibration metric resulted in better reproduction of the flood probability model estimated from the historical data while reproduction of the recorded hydrographs (i.e. the first calibration metric) did not ensure reproduction of the flood probability model