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50 results for “Process management”
BSC Post-processed Seasonal Climate Forecast for vineyard management
<p>The Climate Services Team at the Barcelona Supercomputing Center has deployed a climate service for vineyard management in the context of the vitiGEOSS project. This dataset results from post-processing, i.e. by downscaling, calibrating and assessing, the seasonal climate prediction system SEAS5 (ECMWF).</p> <p>Probabilistic predictions have as output several solutions (ensemble members) to account for forecast uncertainty. The forecast information is conveyed as probabilities, in this case as the probabilities of occurrence of three categories or terciles (below normal, normal and above normal). The categories are defined based on the terciles of the model climatology distribution over a period in the past. Additional information regarding the probability of occurrence of extremes is also provided, considered as the probability of not reaching the 10th percentile or surpassing the 90th percentile of the model climatology distribution. The skill scores provide information on the forecast quality (fair Ranked Probability Skill Score for the tercile categories and fair Brier Skill Score for the probabilities of extremes). A positive skill score indicates that the prediction is good (better than using average past conditions) in the long term. In contrast, a negative skill score indicates a prediction is not beating the climatological forecast.</p> <ul> <li> <p>Prediction system: European Center for Medium-Range Weather Forecasts (ECMWF) SEAS5 and post-processed by BSC.</p> </li> <li> <p>Issue frequency: Monthly (~15th of each month)</p> </li> <li> <p>Lead times: months 1 to 3 (e.g. For a forecast initialised in June, forecasts will be monthly averages for July, August and September). The initialization date is indicated in the name of each file (e.g. 20210701). </p> </li> <li> <p>Variables: mean, minimum and maximum 2 m temperature, accumulated precipitation, incoming solar radiation</p> </li> <li> <p>Ensemble size: 51 members</p> </li> <li> <p>Postprocessing: Downscaling from original (1°x 1°) resolution to 0.1°x 0.1° for three domains and monthly calibration with variance inflation. </p> </li> <li> <p>Spatial coverage of the domains: </p> </li> <ul> <li> <p>Catalonia region is indicated by ‘cat’ and covers latitudes [10 N, 44 N], and longitudes [1 W, 4 E]. The latitude indices range [1:41], and the longitude indices range [1:51].</p> </li> <li> <p>Douro region is indicated by ’douro’ and covers latitudes [40 N, 43N ] and longitudes [9 W, 6 W]. The latitude indices range [1:31], and the longitude indices range [1:31].</p> </li> <li> <p>Campana region is indicated by ‘campania’ and covers latitudes [39 N, 43 N] and longitudes [13 E,17.3 E]. The latitude indices range [1:41], and the longitude indices range [1:44]. </p> </li> </ul> </ul> <p> </p> <p>For each prediction, there are several files containing the seasonal variables values, probabilities, definition of the categories and skill scores.</p> <ul> <li> <p>Forecast probabilities</p> </li> </ul> <p>E.g t2_campania_prob_20210701.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘prob’ and the initialization date of the forecasts (1st of the month).</p> <p>It contains the forecast probabilities in (%) of each tercile category below normal (prob_bn), normal (prob_n) and above normal (prob_an) and the probability of lower extreme (prob_bp10) and the probability of upper extreme (prob_ap90). The latitude, longitude, and lead time (months 1 to 3) can be selected.</p> <p><strong> </strong></p> <ul> <li> <p>Forecast ensemble members</p> </li> </ul> <p> E.g. t2_campania_20210701.ncml</p> <p>The file name contains the name of the variable, domain and initialization date of the forecasts (1st of the month).</p> <p>It contains the 51 absolute values of the forecast variables in their corresponding units (see Table 2). The latitude, longitude, and lead time (months 1 to 3) can be selected.</p> <p><strong> </strong></p> <ul> <li> <p>Category limits</p> </li> </ul> <p>E.g. t2_campania-percentiles_month07.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘percentiles’ and the month for which the category limits apply. </p> <p>It contains the limits of the predicted categories ( below normal, normal and above normal). These categories are defined with respect to a period in the past. The 33rd, 66th percentiles (p33 and p66) divide the model climatological distribution into 3 equiprobable categories. The 33th percentile is the boundary between below normal and normal, and the 66th percentile is the boundary between the normal and above normal categories. The 10th and 90th percentiles, which define the threshold for the lower and upper extreme conditions, are also provided (p10 and p90). It should be noted that the definition of the categories is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p> <p><strong> </strong></p> <ul> <li> <p>Skill scores</p> </li> </ul> <p>E.g t2_campania-skill_month07.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘skill’ and the month for which the skill scores apply. </p> <p>It contains the measures of forecast quality, the fair Ranked probability score for terciles (rpss) and the fair Brier Skill Score for lower and upper extremes (bsp10 and bsp90). It should be noted that the skill level is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p>
Intertransverse Process Block to Improve Quality of Recovery and Pain Management in Adult Cardiac Surgical Patients
ClinicalTrials.gov study NCT06946290. IPD Sharing: NO. Countries: 0. Publications: 40.
Effect of Lactation Management Model on Breastfeeding Process
ClinicalTrials.gov study NCT04593719. IPD Sharing: NO. Countries: 1. Publications: 5.
Problem Management Plus With Emotional Processing (PM+EP) for Forcibly Displaced Youth
ClinicalTrials.gov study NCT06878092. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Digital Therapeutic in Whole Process Management of Lung Cancer
ClinicalTrials.gov study NCT06230445. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Nursing Intervention Program in the Management of Parental Anxiety and Infant Pain in the Surgical Process of Children to be Circumcised
ClinicalTrials.gov study NCT05387291. IPD Sharing: NO. Countries: 1. Publications: 0.
Process Versus Outcomes Incentives for Lipid Management
ClinicalTrials.gov study NCT02246959. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Influence of Standardized Process Management of Laryngeal Mask Airway Placement Based on Pressure Monitoring on the Incidence of Adverse Reactions in Elderly Patients During the Perioperative Peri
ClinicalTrials.gov study NCT06954857. IPD Sharing: NO. Countries: 1. Publications: 0.
Management of Major Trauma Patients at Aarau Trauma Center - Evaluation of Processes and Patient Outcome
ClinicalTrials.gov study NCT02165137. IPD Sharing: Not stated. Countries: 1. Publications: 23.
THE IMPORTANCE OF TIME MANAGEMENT IN TEACHING PROCESS
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BSC Post-processed Sub-seasonal Climate Forecast for vineyard management
<p>The Climate Services Team at the Barcelona Supercomputing Center has deployed a climate service for vineyard management in the context of the vitiGEOSS project. This dataset results from post-processing, i.e. by downscaling, calibrating and assessing, the subeasonal climate prediction system NCEP-CFSv2.</p> <p>Probabilistic predictions have as output several solutions (ensemble members) to account for forecast uncertainty. The forecast information is conveyed as probabilities, in this case as the probabilities of occurrence of three categories or terciles (below normal, normal and above normal). The categories are defined based on the terciles of the model climatology distribution over a period in the past. Additional information regarding the probability of occurrence of extremes is also provided, considered as the probability of not reaching the 10th percentile or surpassing the 90th percentile of the model climatology distribution. The skill scores provide information on the forecast quality (fair Ranked Probability Skill Score for the tercile categories and fair Brier Skill Score for the probabilities of extremes). A positive skill score indicates that the prediction is good (better than using average past conditions) in the long term, while a negative skill score indicates a prediction is not beating the climatological forecast.</p> <ul> <li> <p>Prediction system: National Centers for Environmental Prediction (NCEP) CFSv2, post-processed by BSC (create a lagged ensemble, downscaling and calibration).</p> </li> <li> <p>Issue frequency: Weekly (Initialization every Thursday, post-processed prediction every Friday).</p> </li> <li> <p>Lead times: weeks 1 to 4 (e.g. For a forecast issued on Friday 4th November, forecasts will be weekly averages starting the following Monday-Thursday and the 4 following weeks (e.g. week 1 will be 8th-15th November). The initialization date is indicated in the name of each file (e.g. 20211104). </p> </li> <li> <p>Variables: mean, minimum and maximum 2 m temperature, accumulated precipitation, and incoming solar radiation.</p> </li> <li> <p>Ensemble size: 48 members</p> </li> <li> <p>Postprocessing: Create a lagged ensemble of 48 ensemble members, downscaling from the original (1°x 1°) resolution to 0.1°x 0.1° for the three domains and weekly calibration with variance inflation. </p> </li> <li> <p>Spatial coverage of the domains: </p> </li> <ul> <li> <p>Catalonia region is indicated by ‘cat’ and covers latitudes [10 N, 44 N], and longitudes [1 W, 4 E]. The latitude indices range [1:41], and the longitude indices range [1:51].</p> </li> <li> <p>Douro region is indicated by ‘douro’ and covers latitudes [40 N, 43N ] and longitudes [9 W, 6 W]. The latitude indices range [1:31], and the longitude indices range [1:31].</p> </li> <li> <p>Campana region is indicated by ‘campania’ and covers latitudes [39 N, 43 N] and longitudes [13 E,17.3 E]. The latitude indices range [1:41], and the longitude indices range [1:44]. </p> </li> </ul> </ul> <p>The specific latitude and longitude indices to extract the predictions corresponding to each vitiGEOSS site are indicated in Table 2.</p> <ul> <li> <p>Forecast probabilities</p> </li> </ul> <p>E.g t2_campania_prob_20211104.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘prob’ and the initialization date of the forecasts (Always a Thursday).</p> <p>It contains the forecast probabilities in (%) of each tercile category below normal (prob_bn), normal (prob_n) and above normal (prob_an) and the probability of lower extreme (prob_bp10) and the probability of upper extreme (prob_ap90). The latitude, longitude and lead time (weeks 1 to 4) can be selected.</p> <ul> <li> <p>Forecast ensemble members</p> </li> </ul> <p> E.g. t2_campania_20211104.ncml</p> <p>The file name contains the name of the variable, domain and initialization date of the forecasts (Always a Thursday).</p> <p>It contains the 48 absolute values of the forecast variables in their corresponding units (see Table 2). The latitude, longitude and lead time (weeks 1 to 4) can be selected.</p> <ul> <li> <p>Category limits</p> </li> </ul> <p>E.g. t2_campania_percentiles_week44.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘percentiles’ and the month for which the category limits apply. </p> <p>It contains the limits of the predicted categories ( below normal, normal and above normal). These categories are defined with respect to a period in the past. The 33rd, 66th percentiles (p33 and p66) divide the model climatological distribution into 3 equiprobable categories. The 33rd percentile is the boundary between below-normal and normal, and the 66th percentile is the boundary between the normal and above-normal categories. The 10th and 90th percentiles, which define the threshold for the lower and upper extreme conditions, are also provided (p10 and p90). It should be noted that the definition of the categories is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p> <ul> <li> <p>Skill scores</p> </li> </ul> <p>E.g t2_campania_skill_week44.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘skill’ and the week of the year for which the skill scores apply. </p> <p>It contains the measures of forecast quality, the fair Ranked probability score for terciles (rpss) and the fair Brier Skill Score for lower and upper extremes (bsp10 and bsp90). It should be noted that the skill level is specific to each location (latitude and longitude), initialization and lead time (valid week).</p>
THE PROCESS OF MANAGEMENT DECISION MAKING IN ORGANIZATIONS
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Business process management concept. Bibliographic collection from Web of Science (March 21, 2023).
<p>The search query: https://www.webofscience.com/wos/woscc/summary/f99d7b5d-a7bb-4c35-a8ba-d45042c4e0a9-7ab6bd8b/relevance/1. Contains 95 articles (adjusted after screening the relevance of titles, abstracts, keywords for the research purposes).</p>
Building an Architected Cyber Security Management Process Model Integrated with Maturity and Effectiveness Measurement
<p>The traditional approach to managing a cyber security program is to implement an Information Security Management System (ISMS), based on one or a combination of standards such as ISO 27001, COBIT, or the NIST Cyber Security Framework. The effectiveness of the program may then be assessed with annual audits. However, the ongoing success of cyber attacks through phishing, ransomware and other means as demonstrated through data breach reporting suggests that the current approach to managing the cyber security program still needs improvement. To address this issue, we present an architected process model for cyber security management which through its design enables measurement of both its process maturity and control effectiveness. The model comprises 6 strategic processes, 13 tactical processes and 21 operational processes. To verify the usefulness of this operating model, we tested it with a multinational company. The case study reveals that the proposed model can provide valuable and instructive insights for managing cyber security within an organization. By adopting the model, organizations can enhance cyber security assurance and identify pathways to increase the effectiveness and maturity of their cyber security program.</p>
Process and Outcomes of Pain Management
ClinicalTrials.gov study NCT00028249. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Avoiding tipping points in fisheries management through Gaussian process dynamic programming
Model uncertainty and limited data are fundamental challenges to robust management of human intervention in a natural system. These challenges are acutely highlighted by concerns that many ecological systems may contain tipping points, such as Allee population sizes. Before a collapse, we do not know where the tipping points lie, if they exist at all. Hence, we know neither a complete model of the system dynamics nor do we have access to data in some large region of state space where such a tipping point might exist. We illustrate how a Bayesian non-parametric approach using a Gaussian process (GP) prior provides a flexible representation of this inherent uncertainty. We embed GPs in a stochastic dynamic programming framework in order to make robust management predictions with both model uncertainty and limited data. We use simulations to evaluate this approach as compared with the standard approach of using model selection to choose from a set of candidate models. We find that model selection erroneously favours models without tipping points, leading to harvest policies that guarantee extinction. The Gaussian process dynamic programming (GPDP) performs nearly as well as the true model and significantly outperforms standard approaches. We illustrate this using examples of simulated single-species dynamics, where the standard model selection approach should be most effective and find that it still fails to account for uncertainty appropriately and leads to population crashes, while management based on the GPDP does not, as it does not underestimate the uncertainty outside of the observed data.
Figure 2 from: Véron R, Fernando N, Narayanan N, Upreti B, Ambat B, Pallawala R, Rajbhandari S, Rao Dhananka S, Zurbrügg C (2018) Social processes in post-crisis municipal solid waste management innovations: A proposal for research and knowledge exchange in South Asia. Research Ideas and Outcomes 4: e31430. https://doi.org/10.3897/rio.4.e31430
Figure 2 Partly decentralized waste chains in Kerala.
Figure 1 from: Véron R, Fernando N, Narayanan N, Upreti B, Ambat B, Pallawala R, Rajbhandari S, Rao Dhananka S, Zurbrügg C (2018) Social processes in post-crisis municipal solid waste management innovations: A proposal for research and knowledge exchange in South Asia. Research Ideas and Outcomes 4: e31430. https://doi.org/10.3897/rio.4.e31430
Figure 1 Schematic representation of the institutional MSWM architecture.
Visual Management for the Radiotherapy Process
ClinicalTrials.gov study NCT01854541. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Food Trial Evaluating the Efficacy of SBD111 Versus Placebo for the Clinical Dietary Management of the Metabolic Processes of Osteopenia
ClinicalTrials.gov study NCT05009875. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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International Brain Laboratory public data
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OpenNeuro
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