Journal articles on the topic 'Human values dashboard'

ABSTRACT This study examines aspects of dual performance measures in the context of organizations disclosing operational performance to the general public through information dashboards. Dual performance measures are measures where performance is a function of two values, one value denoting the percentage of a group to which the measure refers and one value denoting the performance level achieved by that particular percentage. Dual measures must be anchored to either target percentage or target performance level before a decision on over- or under-performance can be made. A 2 × 2 experiment (n = 222), involving performance assessment of a fictional emergency room, varies anchor and presentation format, and measures the effects on subjective performance of the emergency room, as well as perceived informativeness and attractiveness of the dashboard. The results indicate, first, that choice of anchor matters, in the sense that anchor choice can mask or accentuate relevant information, thereby influencing subjective performance. Second, a pictorial unit chart combined with a performance-level anchor is perceived to be the most informative and most attractive dashboard display. The study contributes to research on the design of information dashboards by developing theory on the effectiveness of reporting dual performance measures.

A dashboard application is proposed and developed to act as a Digital Twin that would indicate the Measured Value to be held accountable for any future failures. The current study describes a method for the exploitation of historical data that are related to production performance and aggregated from IoT, to eliciting the future behavior of the production, while indicating the measured values that are responsible for negative production performance, without training. The dashboard is implemented in the Java programming language, while information is stored into a Database that is aggregated by an Online Analytical Processing (OLAP) server. This achieves easy Key Performance Indicators (KPIs) visualization through the dashboard. Finally, indicative cases of a simulated transfer line are presented and numerical examples are given for validation and demonstration purposes. The need for human intervention is pointed out.

BackgroundDashboards are visual information systems frequently employed by healthcare organisations to track key quality improvement and patient safety performance metrics. The typical healthcare dashboard focuses on specific metrics, disease processes or units within a larger healthcare organisation. Here, we describe the development of a visual analytical solution (keystone dashboard) for monitoring an entire healthcare organisation.MethodsThe improvement team reviewed and assessed various data sources across the organisation and selected a group of patient and employee related metrics that afforded a broad overview of the organisation’s well-being. Metrics spanned the organisation and included data from patient safety, quality improvement, human resources, risk management and medical staff affairs. Each metric was assigned a numeric weight that correlated with its impact. A visual model incorporating the various data fields was then constructed.ResultsThe keystone dashboard incorporates a data heatmap and density visualisation to emphasis areas of higher density and/or weighted values. The heatmap is used to indicate the weight/magnitude of each metric within a data range in two dimensions: location and time. The visualisation ‘heats up’ depending on the combination of counts events and their assigned impact for the reporting month. Most data sources update in near real time.SummaryThe keystone dashboard serves as a comprehensive and collaborative integration of data from patient safety, quality improvement, human resources, risk management and medical staff affairs. This visual analytical solution incorporates and analyses metrics into a single view with the intent of providing valuable insight into the health of an entire organisation. This dashboard is unique as it provides a broad overview of a healthcare organisation by incorporating key metrics that span the organisation.

Crop production is only profitable when all information is known about a specific crop. This information is turned into profitable yield through efficient management technologies and critical decision-making. Efficient management can only be done with the help of yield monitoring technologies, with the goal to optimize natural, human, and material resources while maximizing crop yield efficiency. Yield monitoring technologies works using sensors systems and ensure accuracy of yield. The sensor system detects every aspect of a potential yield in seconds and has also the ability to measure yield values from raw data. and set formulas. The final measurement (yield) is calculated and displayed on the dashboard of relevant technologies and acts to help decision-making and methodology.

Abstract Objective Patient-reported outcomes (PROs) are a valued source of health information, but prior work focuses largely on data capture without guidance on visual displays that promote effective PRO use in patient-centered care. We engaged patients, providers, and design experts in human-centered design of “PRO dashboards” that illustrate trends in health-related quality of life (HRQOL) reported by patients following prostate cancer treatment. Materials and Methods We designed and assessed the feasibility of integrating dashboards into care in 3 steps: (1) capture PRO needs of patients and providers through focus groups and interviews; (2) iteratively build and refine a prototype dashboard; and (3) pilot test dashboards with patients and their provider during follow-up care. Results Focus groups ( n = 60 patients) prioritized needs for dashboards that compared longitudinal trends in patients’ HRQOL with “men like me.” Of the candidate dashboard designs, 50 patients and 50 providers rated pictographs less helpful than bar charts, line graphs, or tables ( P < .001) and preferred bar charts and line graphs most. Given these needs and the design recommendations from our Patient Advisory Board ( n = 7) and design experts ( n = 7), we built and refined a prototype that charts patients’ HRQOL compared with age- and treatment-matched patients in personalized dashboards. Pilot testing dashboard use ( n = 12 patients) improved compliance with quality indicators for prostate cancer care ( P < .01). Conclusion PRO dashboards are a promising approach for integrating patient-generated data into prostate cancer care. Informed by human-centered design principles, this work establishes guidance on dashboard content, tailoring, and clinical use that patients and providers find meaningful.

In today’s global scenario, with the evolution of new technologies and robust ideas, the world gets more involved and embed the advancements of wireless communication with information technology. An ongoing Gartner report assesses that, by 2021, there will be 25.1 billion web associated gadgets, developing at a pace of 32% every year. Bounties of automation are minimizing the human assistance, intervention and reduced risk factor in industry. Here Industrial Automation is used to control systems or things such as computers or robots or machines or sensors with the help of Internet protocol and cloud computing. In this paper six parameters viz., vibration, temperature, humidity, air quality, sound rate and pressure are monitored and controlled remotely using cloud computing. The system performance automatically changes on the basis of sensor data being collected at regular intervals with a feedback mechanism, thereby allowing the system to control or monitor various devices using internet protocols. The threshold values for all the sensors are set as per the industrial standards. These automation techniques find extensive applications in various control mechanisms to operate the equipment under production processes like boilers and heat-treating ovens, steering and stabilization, pressure exerted by ideal gases in confined containers, vibrations by machinery, air pollution released from chemical composites etc.,

Usability evaluation traditionally relies on costly and time-consuming human-subject experiments, which typically involve developing physical prototypes, designing usability experiment, and recruiting human subjects. To minimize the limitations of human-subject experiments, computational human performance models can be used as an alternative. Human performance models generate digital simulations of human performance and examine the underlying psychological and physiological mechanisms to help understand and predict human performance. A variety of in-vehicle information systems (IVISs) using advanced automotive technologies have been developed to improve driver interactions with the in-vehicle systems. Numerous studies have used human subjects to evaluate in-vehicle human-system interactions; however, there are few modeling studies to estimate and simulate human performance, especially in in-vehicle manual and speech interactions. This paper presents a computational human performance modeling study for a usability test of IVISs using manual and speech interactions. Specifically, the model was aimed to generate digital simulations of human performance for a driver seat adjustment task to decrease the comfort level of a part of driver seat (i.e., the lower lumbar), using three different IVIS controls: direct-manual, indirect-manual, and voice controls. The direct-manual control is an input method to press buttons on the touchscreen display located on the center stack in the vehicle. The indirect-manual control is to press physical buttons mounted on the steering wheel to control a small display in the dashboard-cluster, which requires confirming visual feedback on the cluster display located on the dashboard. The voice control is to say a voice command, “ deflate lower lumbar” through an in-vehicle speaker. The model was developed to estimate task completion time and workload for the driver seat adjustment task, using the Queueing Network cognitive architecture (Liu, Feyen, & Tsimhoni, 2006). Processing times in the model were recorded every 50 msec and used as the estimates of task completion time. The estimated workload was measured by percentage utilization of servers used in the architecture. After the model was developed, the model was evaluated using an empirical data set of thirty-five human subjects from Chen, Tonshal, Rankin, & Feng (2016), in which the task completion times for the driver seat adjustment task using commercial in-vehicle systems (i.e., SYNC with MyFord Touch) were recorded. Driver workload was measured by NASA’s task load index (TLX). The average of the values from the NASA-TLX’s six categories was used to compare to the model’s estimated workload. The model produced results similar to actual human performance (i.e., task completion time, workload). The real-world engineering example presented in this study contributes to the literature of computational human performance modeling research.

Abstract:Many critics of economic analysis of climate change impacts and response options claim that information is needed on ecosystem characteristics as well as on economic values to fully inform decisions about how climate change affects human well-being. Information on the irreversibility of impacts also is important, critics argue, because it relates to how society evaluates implications for intergenerational equity. In addition, because climate change is subject to a large degree of Knightian uncertainty, it is useful to understand both the information available for assessing climate change risks, and how individuals themselves perceive and evaluate risks. The paper discusses rationales for using these types of information as important complements to benefit-cost analysis for evaluating climate change risks and responses. Ideally such information could be available in a “dashboard” for decision makers assessing social and economic impacts, although limits on currently available information are a significant barrier to using that approach.

This paper discusses the findings of thermal comfort assessment at Malaysian automotive industry. Nine critical workstations were chosen as subjects for the study in order to determine the thermal comfort among workers at Malaysian automotive industry. The human subjects for the study comprises of the operators from tire receiving, dashboard assembly, drum tester, body assembly, seat assembly, door check assembly, stamping workstation, engine sub assembly and paint shop of the factory. The environmental factors such as Wet Bulb Globe Temperature (WBGT), relative humidity, air velocity, illuminance were measured using BABUC A apparatus and Thermal Comfort Measurement equipment. Through questionnaire survey, the demographic data of subjects and their perceptions on thermal comfort at each workstation were assessed based on ISO Standard 7730 and thermal sensation scale using Predicted Mean Vote (PMV). Then, Predicted Percentage of Dissatisfied (PPD) is used to estimate the thermal satisfaction of occupants. The results indicated that most of the workstations of the automotive industry are considered as uncomfortable. Tire receiving station is considered having better working environment compared to other stations with lowest PMV index of 1.09 to 1.41 and PPD of 46%. Meanwhile, the engine sub assembly station and paint shop of assembly are considered the worst thermal environment with the PMV index values ranging between 2.1 to 2.9 and PPD values of 81% to 99%. Therefore, these two workstations are considered not comfortable because the thermal sensation scale is warm and almost hot.

Abstract. Quantifying impacts on the environment and human health is a critical requirement for geological subsurface utilisation projects. In practice, an easily accessible interface for operators and regulators is needed so that risks can be monitored, managed, and mitigated. The primary goal of this work was to create an environmental hazards quantification toolkit as part of a risk assessment for in-situ coal conversion at two European study areas: the Kardia lignite mine in Greece and the Máza-Váralja hard coal deposit in Hungary, with complex geological settings. A substantial rock volume is extracted during this operation, and a contaminant pool is potentially left behind, which may put the freshwater aquifers and existing infrastructure at the surface at risk. The data-driven, predictive tool is outlined exemplary in this paper for the Kardia contaminant transport model. Three input parameters were varied in a previous scenario analysis: the hydraulic conductivity, as well as the solute dispersivity and retardation coefficient. Numerical models are computationally intensive, so the number of simulations that can be performed for scenario analyses is limited. The presented approach overcomes these limitations by instead using surrogate models to determine the probability and severity of each hazard. Different surrogates based on look-up tables or machine learning algorithms were tested for their simplicity, goodness of fit, and efficiency. The best performing surrogate was then used to develop an interactive dashboard for visualising the hazard probability distributions. The machine learning surrogates performed best on the data with coefficients of determination R2>0.98, and were able to make the predictions quasi-instantaneously. The retardation coefficient was identified as the most influential parameter, which was also visualised using the toolkit dashboard. It showed that the median values for the contaminant concentrations in the nearby aquifer varied by five orders of magnitude depending on whether the lower or upper retardation range was chosen. The flexibility of this approach to update parameter uncertainties as needed can significantly increase the quality of predictions and the value of risk assessments. In principle, this newly developed tool can be used as a basis for similar hazard quantification activities.

OBJECTIVES/SPECIFIC AIMS: A growing concern about the declining physician-scientist workforce prompted the 2014 National Institutes of Health (NIH) Physician Scientist Workforce to recommended that “tools for assessing the strength of the biomedical workforce” be developed. To aid strategic planning, the Oregon Clinical and Translational Research Institute convened key stakeholders at its home university, Oregon Health and Science University (OHSU), to survey the local landscape of physician scientists. Surprisingly, few consensus methods were available to measure and benchmark OHSU with respect to national comparators. To address this deficit, we sought to develop clear and objective metrics describing physician-scientist success at our institution. By focusing on local funding, we were able to generate more complete and robust data than others have reported. These data also permit us to compare ourselves to the national workforce, using well-curated and accessible national databases. The goal of the analyses is to contribute to strategic decision-making by portraying the local physician-scientist workforce, comparing it to the national landscape, and making recommendations about mechanisms to address potential opportunities. This has led us to develop a simple quantitative dashboard, which now permits OHSU to craft strategic targets and address successes and opportunities. These approaches are likely to be valuable elsewhere. METHODS/STUDY POPULATION: OHSU is a medium-sized academic health center in Portland, Oregon with over 1200 principal investigators and over $230M in NIH funding. The primary focus of our investigation was physician-scientists who receive extramural funding. To align with other analyses, we distinguish physician-scientists with an M.D. only, or with an M.D. and a master’s degree, from physician-scientists who hold an M.D./Ph.D. For this distinction, we use the indicator “M.D.-only” to indicate the former. The study design consisted of (a) selection of available and relevant national level data on the physician-scientist workforce, (b) curating of local level data to align it with the national indicators, (c) comparing the 2 sets of data to look for differences in trends over time, and (d) supplementing the analyses with additional local data not available at the national level. Key comparisons were tested for statistical significance and plotted on a dashboard, which was then reviewed by an OHSU internal working group focused on physician-scientists. Data elements included degrees, age, gender, and grants awarded. National data come directly from the NIH Data Book, updated for fiscal year 2016. The NIH makes all funded project data available in the publicly downloadable ExPORTER Data Catalog. These project data were used to supplement the summarized data available from the NIH Data Book, allowing us to extract OHSU investigators and to complete the K to R comparative analysis. For analyses of OHSU investigators holding funding other than RPGs, we relied on institutional data from the OHSU grants and contracts office. Demographic data on OHSU investigators were obtained from departmental and human resource records. The time period for these analyses was 1998–2016. RESULTS/ANTICIPATED RESULTS: At OHSU, as nationally, there has been an increase in RPG-holding Ph.D.s but not in RPG-holding physician-scientists. At OHSU, nearly three-fourth of physician-scientist RPGs hold an M.D.-only degree, compared with nationally, where nearly half of physician-scientists are M.D./Ph.D.s. The percent of younger, early-career, RPG-holding physician-scientists has declined precipitously at OHSU and nationally. At OHSU, the percentage of RPGs held by women physician-scientists is below the national figure. Funding sources for physician-scientists at OHSU were more diverse than for Ph.D. scientists, and physician-scientists comprise the majority of Principal Investigators on clinical trials. These non-RPG sources of funding remain a critical source of support, although local analyses of time spent on research indicate that physician-scientists with NIH funding spend a greater percentage of their time on research than those without. OHSU PI’s have had success in transitioning from K08 and K23 grants to R-level grants, with similar percentages receiving RPGs within 5 years. A dashboard comparing these trends was developed. DISCUSSION/SIGNIFICANCE OF IMPACT: There were 3 key impacts from our analyses. First, we developed and disseminated a dashboard with both local data and national comparators. Second, in consultation with institutional leadership, we selected target values to define success for each metric. Third, we recommended actions that will help OHSU meet the selected targets. A major accomplishment of this structured approach has been the identification of opportunities for change that were not recognized previously. For example, leadership was not aware of the substantial and growing deficit in female physician-scientists at OHSU, compared with the impressive increases nationally. Thus, to reduce gender disparity at OHSU, we have recommended purposeful recruitment; one approach is to target female graduates of Medical Scientist Training Programs for faculty positions, as this group has better success at achieving R-level funding than do M.D.-only applicants. Another outcome is to help set ambitious but reasonable targets for improving the local landscape. Thus, we aim to reduce the average age of RGP-holding physician-scientists at OHSU by one year during the next 5 years. Although reversing current trends will not be easy, our analyses suggest that the average age of RPG level physician-scientists at OHSU would decrease were OHSU were to match the national-level proportions of women and M.D./Ph.D. physician-scientists. In addition to targeting gender disparities, we have recently implemented a program that supplements funding for recruiting young physician scientists, and then supporting their pursuit of RPG funding. Locally, a bright spot is the K to RPG transition rate for K23 awardees, which compare favorably with national data, an outcome that we plan to maintain. In analyzing this area of success, one reason is our strong mentorship program, called OCTRI Scholars, which is provided through our CTSA-sponsored institute. This has fostered an atmosphere of success among young physician-scientists and is one of the reasons that we endorse recommendation #9 from the PSWR, suggesting that Clinical and Translational Science Award (CTSA) Institutes play pivotal roles in monitoring and enhancing the success of the physician-scientist workforce. Thus, several perceived deficiencies might be addressed with adjustment of 1 or 2 specific institutional policies. While the specific opportunities and strengths may be different at other institutions, our proposed dashboard, which couples publicly curated, freely accessible databases, with readily available institutional resources, should help institutions to set and achieve their own goals.

Several studies reported the significant effect of indoor air quality on human health, safety, productivity, and comfort because most humans usually conduct 80%–90% of their activity inside the building. This is generally due to the fact that indoor pollution is associated with volatile organic compounds (VOCs), pollutants with chronic health effects, both non-carcinogenic and carcinogenic, on humans. Therefore, this study focused on developing wireless VOCs sensor nodes with a low-power strategy feature to perform an autonomous operation in indoor air quality monitoring (IAQM). The sensor node mainboard consists of a microcontroller-based AVR (ATmega-4808) that supports a low power mode and low-power IAQ-Core sensor for VOCs detection. The low-power sensing algorithm developed also allowed the sensor node to consume a total power of 0.22 mAh for one cycle of operation, which includes the initial process, TVOCs value reading process, data transmitting process, and low power mode process at a time interval of 30 min. The most significant power was observed to be consumed in the data transmitting process with 0.13 mAh or 58% of total power consumption in one cycle of sensor node operation. Furthermore, the 10F capacitance of the supercapacitor was able to drive the VOCs sensor node for 139 s and it was recommended that further studies use micro energy harvesting (from an indoor environment) to extend its lifetime. The 1541-minute field experiment conducted also showed that TVOCs and CO2 values were successfully measured and displayed over an internet connection on the monitoring terminal dashboard. The recorded real-time TVOCs value of 175 ppb (<200 ppb) indicates good air quality.

433 Background: We previously reported the implementation of a machine learning (ML) model for mortality prediction that was integrated into a CDSS encouraging clinicians to have a SIC with at-risk cancer patients. The clinical utility of a ML model can change after implementation due to fluctuations in the organization’s patient population and clinical practices. It is important to establish a workflow to monitor and continually reinforce ML-powered CDSS to ensure that it continues to benefit patients. We report a workgroup structure that incorporates data driven evaluation of ML model performance and feedback from CDSS end users to optimize the acceptability of the CDSS. Methods: The workflow was piloted in the gastrointestinal (GI) oncology clinic from 11/2021-5/2022. A workgroup including members of the implementation team and end-users of the CDSS met monthly to review 1) a dashboard that displays model performance, 2) an electronic health record (EHR) report that summarizes use of the CDSS, 3) feedback from end users regarding their opinion of the CDSS and any barriers to implementation. We evaluated the accuracy of model predictions among subgroups as defined by mortality and unplanned hospital admissions or ED visit rates. Fisher’s Exact Test was used to identify differences between categorical variables. Numeric values including incidence rate ratios (IRRs) adjusted for age, sex, race, and gender with 95% confidence intervals (CIs) were calculated using Poisson regression. Results: 119 patients were evaluated by the model and 50 (42%) were assessed as high-risk. In the high-risk group, the oncology team evaluated 39 (78%) patients for appropriateness of a SIC; SIC was completed with 5 (10%) patients. During workgroup meetings, physicians shared that some of the high-risk predictions were for patients undergoing curative intent therapy. 0 out of 24 patients who received curative treatment died and 5 out of 26 patients who receive palliative treatment died. The log-rank p-value of 0.03 indicates that the survival distribution differs significantly over time between two groups. The adjusted IRR for unplanned hospital visits (palliative vs curative) was 2.55 (1.3-5.0). Adjusted mean hospital visits per month were 0.34 (0.21-0.51) vs 0.13 (0.06-0.21). Conclusions: The workgroup format is a feasible method to continuously review acceptability of a ML-powered CDSS. It may evaluate critical feedback from end users in a holistic manner that can augment a data driven evaluation of the model performance. The data implies that patients undergoing curative therapy have a decreased risk for mortality and unplanned hospital admissions or ED visits. The CDSS may be optimized by excluding these patients; however, longer follow up of this sub-population is needed to confirm that they have no additional risk factors.

Background: In 2018, the Ministry of Health (MOH) in Saudi Arabia launched the Infection Control Excellence Center (ICEC) program among healthcare governmental institutions to create an exceptionally high concentration of expertise and resources within the infection prevention and control discipline to afford the best patient outcomes possible. King Saud Medical City (KSMC), one of the main healthcare institutions in Riyadh, was selected to be among the 10 facilities participating in ICEC 2019 competition. It is expected to qualify the facility to lead the Kingdom infection prevention and control as well as sharing expertise at regional and international levels. Methods: The infection control team at KSMCA used a business model canvas to present the project vision, resources, partners, values, and revenue streams (Fig. 1). All project stakeholders were engaged, including core infection control team, various hospital departments as internal partners, along with the MOH team as external partners. The ICEC program was presented at the KSMC executive council to earn leadership support. The following assessment areas were included in the presentation: (1) quality assurance and patient care through sustain basic infection control standards and improve key performance indicators (KPIs); (2) enhance the development and structure of the infection control team; (3) pursue innovative ideas in infection control practices. Overall, 17 projects arranged into 4 programs have been proposed (Fig. 2). Results: The institution successfully passed the eligibility criteria assessment in the first quarter of 2019. Infection control KPIs have been corporatized with KSMC strategic KPIs that support infection control improvement initiatives. The infection control team continues to grow in function and capacity. Also, 4 additional were awarded CIC certification in 2019 to reach total of 11 CICs, which represent 30% of the team (including 1 recertification). A dashboard designed by the project management office facilitates follow-up with the proposed projects in progress. Completion levels ranging between 30% and 100% have been achieved among these projects. A final evaluation was conducted in December 2019, including a field visit by the MOH ICEC team as well as a written MCQs exams and interviews with the core infection control team. Communication among the stakeholders and leadership involvement were considered among the assessment criteria. Conclusions: The ICEC supports and motivates investment in human capital and encourages innovative, cost-effective solutions in infection control field in Saudi Arabia. It is also aligned with Saudi Arabia healthcare transformation and the 2030 vision through integrated programs in healthcare facilities.Funding: NoneDisclosures: None

If we observe top companies in any industry, we notice they have one thing in common: innovation. Successful business leaders recognize when the same ideas and methods used before aren’t working anymore. Smart, innovative approaches are needed for our hybrid working environment. The ABCD business model shows that present organizations spend the majority of their time on activities related to business administration (A) and doing repetitive work (D). The rest of the time is allocated to dealing with crises (C), and only nominally to improving the ways business is done (B). Digital transformation, competition, and the need for organizations to leverage technology and innovation in the future will ‘force’ organizations to maintain A, increase B, and (strategize how to) decrease C and D. Two initiatives will be unpacked and common elements will be identified as indicators in improving B. Five ways to change the game and become a human-focused organization that promotes innovation are proposed based on our learnings: People: Encourage a growth mindset of continuous learning, creativity in how problems are solved, and flexibility how work gets done Encourage innovative thinking; create innovative groups Build skills, e.g., analytical thinking, innovation, creativity, and initiative Workplace: Design a psychologically safe culture, where people are included, can learn, have a sense of belonging, are appreciated, and valued for who they are and what they contribute and challenge. Technology: Create an experimentation lab to TRY-TEST-ADAPT in rapid cycles to learn and fail/learn fast or advance the innovation. We are faced with multiple, messy issues that require out-of-the-box thinking and innovative solutions. Capturing lessons learned can build leading indicators that will help improve B. A simulation dashboard that quantifies the change is an innovation tool we plan to develop.

The bdverse is a collection of packages that form a general framework for facilitating biodiversity science in R (programming language). Exploratory and diagnostic visualization can unveil hidden patterns and anomalies in data and allow quick and efficient exploration of massive datasets. The development of an interactive yet flexible dashboard that can be easily deployed locally or remotely is a highly valuable biodiversity informatics tool. To this end, we have developed 'bddashboard', which serves as an agile framework for biodiversity dashboard development. This project is built in R, using the Shiny package (RStudio, Inc 2021) that helps build interactive web apps in R. The following key components were developed: Core Interactive Components The basic building blocks of every dashboard are interactive plots, maps, and tables. We have explored all major visualization libraries in R and have concluded that 'plotly' (Sievert 2020) is the most mature and showcases the best value for effort. Additionally, we have concluded that 'leaflet' (Graul 2016) shows the most diverse and high-quality mapping features, and DT (DataTables library) (Xie et al. 2021) is best for rendering tabular data. Each component was modularized to better adjust it for biodiversity data and to enhance its flexibility. Field Selector The field selector is a unique module that makes each interactive component much more versatile. Users have different data and needs; thus, every combination or selection of fields can tell a different story. The field selector allows users to change the X and Y axis on plots, to choose the columns that are visible on a table, and to easily control map settings. All that in real-time, without reloading the page or disturbing the reactivity. The field selector automatically detects how many columns a plot needs and what type of columns can be passed to the X-axis or Y-axis. The field selector also displays the completeness of each field. Plot Navigation We developed the plot navigation module to prevent unwanted extreme cases. Technically, drawing 1,000 bars on a single bar plot is possible, but this visualization is not human-friendly. Navigation allows users to decide how many values they want to see on a single plot. This technique allows for fast drawing of extensive datasets without affecting page reactivity, dramatically improving performance and functioning as a fail-safe mechanism. Reactivity Reactivity creates the connection between different components. The changes in input values automatically flow to the plots, text, maps, and tables that use the input, and cause them to update. Reactivity facilitates drilling down functionality, which enhances the user’s ability to explore and investigate the data. We developed a novel and robust reactivity technique that allows us to add a new component and effectively connect it with all existing components within a dashboard tab, using only one line of code. Generic Biodiversity Tabs We developed five useful dashboard tabs (Fig. 1): (i) the Data Summary tab to give a quick overview of a dataset; (ii) the Data Completeness tab helps users get valuable information about missing records and missing Darwin Core fields; (iii) the Spatial tab is dedicated to spatial visualizations; (iv) the Taxonomic tab is designed to visualize taxonomy; and (v) the Temporal tab is designed to visualize time-related aspects. Performance and Agility To make a dashboard work smoothly and react quickly, hundreds of small and large modules, functions, and techniques must work together. Our goal was to minimize dashboard latency and maximize its data capacity. We used asynchronous modules to write non-blocking code, clusters in map components, and preprocessing and filtering data before passing it to plots to reduce the load. The 'bddashboard' package modularized architecture allows us to develop completely different interactive and reactive dashboards within mere minutes.

ZusammenfassungBeim Human-Biomonitoring wird die innere Schadstoffbelastung des Menschen aus verschiedenen Quellen wie Nahrung, Alltagsgegenständen oder Atemluft erfasst, indem z. B. Blut und Urin analysiert werden. Um das Human-Biomonitoring in Europa zu fördern und zu koordinieren, wurde 2017 das Projekt „Human-Biomonitoring für Europa“ (HBM4EU) begonnen, an dem sich 30 Länder, die Europäische Umweltagentur und die Europäische Kommission beteiligt haben. Im Juni 2022 wurde das Projekt abgeschlossen.Vergleichbare und zuverlässige Belastungsdaten konnten für eine breite Palette von Umweltchemikalien erfasst und einheitlich bewertet werden. Weitere wichtige Erfolge der Initiative waren die Etablierung eines Kontrollprogramms zur Qualitätssicherung, ein Konzept zur Vereinheitlichung zukünftiger HBM-Studien, eine gemeinsame Strategie zur Ableitung von gesundheitsbezogenen Beurteilungswerten (HBM Guidance Values – HBM-GVs) und die Einrichtung nationaler Gremien. Die gewonnenen Belastungsdaten sind über die Informationsplattform für die Überwachung von Chemikalien (IPCHEM) und das EU HBM-Dashboard zugänglich. Publikationen sind über die HBM4EU-Onlinebibliothek frei verfügbar.Insgesamt zeigen die Ergebnisse, dass die Belastungen der EU-Bevölkerung für viele Chemikalien wie etwa Phthalate und perfluorierte Alkylsubstanzen (PFAS) zu hoch sind und weiterhin Handlungsbedarf seitens der Politik besteht. Das im Projekt HBM4EU generierte Wissen kann die politischen Entscheidungsträger:innen bei der Verbesserung der Chemikalien‑, Umwelt- und Gesundheitspolitik unterstützen.

Air pollution in major cities worldwide today has become an important topic due to its adverse effects on human health and the environment. This research aims to monitor particulate matter (PM) at different sizes, i.e., less than 1 μm (PM1), 2.5 μm (PM2.5), 4 μm (PM4), and 10 μm (PM10), and that the concentration of particulate matter changes with location and time. Also, particulate matter is one of the primary pollutants in the air, which affects the environment and the risk of human mortality and morbidity of respiratory disease. This research presents the design and development of a low-cost network using LoRa (short for long-range), a spread spectrum modulation technique derived from Chirp Spread Spectrum (CSS) technology. Semtech’s LoRa is a long-range, low power wireless platform that has become the de-facto wireless platform of the Internet of Things (IoT). For detecting particulate matter levels, a commercially available Sensirion sensor (SPS30) was purchased and used. The developed and the deployed network has these sensors connected to LoRa modules (senor nodes) with an ESP32 microcontroller programmed to collect and send data to a gateway using the 915 MHz frequency band. The gateway then sends the data to ‘The Things Network (TTN)’, where a developed cloud-based dashboard reads the data. Several sensor nodes collect the measured values in the air at different elevations at the monitoring location. The proposed network design has been implemented at a specified location in Auckland City Centre, New Zealand. The designed network system allows the users to access a developed online dashboard, which shows the different concentration levels of particulate matter in the air in real-time.

Purpose This paper develops a vector variation score that quantifies the change in an array of data points from period-to-period. The array could be the amounts reported on an income tax return, the closing stock prices for a set of listed companies, the monthly sales amounts for retail locations or the monthly balances in general ledger accounts. Design/methodology/approach The score is grounded in analytic geometry. The angle θ measures whether the changes were uniformly spread across the line items. The item(s) with the largest contribution(s) to the score can be identified. Line items can be weighted such that they contribute less than fully to the score. Findings The method can identify tax returns with large year-on-year changes. The method can identify the fact that the price movements during earnings season are less dependent than is usually the case. The method can identify anomalies in reported sales amounts. The method should be able to identify ledger accounts’ large abnormal changes. Research limitations/implications Auditors will need to be trained to interpret the results and to reduce the number of false positives. Practical implications The score could be used in both external and internal audit applications where auditors want to quantify and rank period-on-period changes in a search for outliers. Originality/value The change score is normalized to the [0, 1] range. The results can be plotted as a polar plot for display on an auditing dashboard. The contribution of a single line item can be calculated and line items can be weighted to prevent them from having an undue influence on the results.

IntroductionWhen reporting disease rates to the public, a health system must take precaution to protect released data from re-identification risks. While specific guidelines and methods vary across data systems and governances 1 , redaction of cells with small values is a key component in any approach for preparing data for public release. These preparations, when conducted manually, have proven to be arduous, time consuming, and prone to human error. Although finding a “small” value (e.g. “< 5 ” ) is straightforward, detecting conditions in which suppressed values could be recalculated from related cells involves human judgement. Objectives and ApproachGuided by the real-world objective to reports the rates of chronic diseases in British Columbia, we aimed to design a reproducible workflow that would augment human decision-making and offer a nimble quality control tool, approachable by epidemiologists without technical background. Our workflow (1) splits data into disease-by-year data frames of a specific form, (2) applies a sequence of algorithms trained to recognize conditions that made recalculation of suppressed values possible and (3) prints a graph for each case of suggested automatic redaction to be confirmed by a human. ResultsThe augmented suppression system was successfully integrated into the maintenance of Chronic Disease Dashboard, an online reporting tool of the Observatory for Population and Public Health designed to address the gap in surveillance of chronic diseases in British Columbia. Anticipating the evolution of suppression logic, we isolated the logical tests responsible for redaction and provided several options to vary the degree of preserved information. Conclusion / ImplicationsInstead of employing a complex generalizable solution, we make a case for organizing the procedure for small cell redaction as a data visualization task, allowing for straightforward quality control of suppression decision and thus more approachable to a non-technical audience, as well as for employing such learning devices as workflow maps and function dependency trees for structuring applied projects and ensuring their reproducibility.

Student-facing learning analytics dashboards have the potential to reconnect students with their purpose for learning, reminding them of their goals and promoting reflection about their learning journey. However, far less is known about the specifics of the relationship between different types of visualisations and data presented in dashboards and their impact on students’ motivation. In this study, we used a Human-Centred Design method across three iterations to (1) understand how students prioritise similar visualisations when presenting different data (2) examine how they interact with these, and (3) propose a dashboard design that would accommodate students’ different motivational needs. In the first iteration, 26 participants ranked their preferred visualisations using paper prototypes; in the second iteration, a digital wireframe was created based on the results from the first iteration to conduct user tests with two participants; and in the third iteration, a high-fidelity prototype was created to reflect findings from the previous iterations. Overall, findings showed that students mostly valued setting goals and monitoring their progress from a multiple goals approach, and were reluctant about comparing their performance with peers due to concerns related to promoting unproductive competition amongst peers and data privacy. Implications for educators and learning designers are discussed.

PurposeCorrect and well-planned maintenance based on modern global methods directly affects efficiency, quality, direct production costs, reliability and profitability. The selection of an optimal policy for maintenance can be a good solution for industrial units. In fact, by managing constraints such as costs, working hours and human workforce causing sudden equipment failure, production and performance can increase.Design/methodology/approachTherefore, in this research a model was presented to select the best maintenance strategy at Kaghaz Kar Kasra Co of Iran. In this study, it was tried to integrate the two techniques of goal programming and the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritize maintenance strategies. First, all factors affecting maintenance were identified, and based on the Best Worst Method (BWM) the degree of their importance was determined.FindingsAfter the evaluation, only 14 criteria in the 4 dimensions of cost, added value, safety and feasibility were selected. The highest points were given to the criteria of equipment cost and depreciation, equipment and personnel performance, equipment installation time and technical feasibility, respectively. In the next stage, using the TOPSIS method the item of maintenance strategy was ranked, and the 3 strategies of preventive maintenance (PM), predictive maintenance (PDM) and corrective maintenance (CM) were chosen. Modeling was performed utilizing a goal programming approach to select the optimal maintenance strategy for 13 devices. All the technical specifications, cost limits and the device time were extracted. After the model was finished and solved the best item for each device was specified.Originality/value1. Developing a goal programming model and decision-making dashboard. 2. Identifying the criteria and factors affecting the selection of the maintenance strategy for paper production Industry