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76 results for “Performance Management”
A Study to Evaluate the Performance and Safety of CBL-101 Versus Vismed® Multi Eye Drops in the Management of Dry Eye
ClinicalTrials.gov study NCT02975102. IPD Sharing: NO. Countries: 2. Publications: 1.
Personalised Risk Assessment in Febrile Illness to Optimise Real-life Management Across the European Union (PERFORM)
ClinicalTrials.gov study NCT03502993. IPD Sharing: NO. Countries: 1. Publications: 2.
Feasibility Study to Evaluate Safety and Device Performance of the Hospital Glucose Management System (HGMS)
ClinicalTrials.gov study NCT01763567. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Management accounting practices mediate the relationship between enterprise resource planning system and sustainability performance under moderating role of corporate governance
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Results of high-performance computing parameter sweeps associated with the Zimbabwe agro-pastoral management model
<p>This dataset includes all the results of the model runs used to explore the parameters in the Zimbabwe Agro-Pastoral Management Model, archived at ComSES.net (<a href="https://doi.org/10.25937/ta23-sn46">https://doi.org/10.25937/ta23-sn46</a>). This model has been created with and for the researcher-farmers of the Muonde Trust (<a href="http://www.muonde.org/">http://www.muonde.org/</a>), a registered Zimbabwean non-governmental organization dedicated to fostering Indigenous innovation. The results in this dataset were generated using the BehaviorSpace functionality in NetLogo, running headless on a high-performance computing cluster (499,200 runs). The dataset includes model output variables summarized for each model run as well as input values for management variables, underlying parameters, and rainfall models for those runs. A detailed description of the model and its variables (including units of measurement where applicable) is available in Eitzel et al. (2020) at DOI: 10.1371/journal.pone.0237638 and in the Overview, Design, and Details documentation on CoMSES.net.</p>
PLAGIARM_A Size Face Male CEO's Performance and Masculinity: The Mediating Effect of Earnings Management
<p>Plagiarm A Size Face Male CEO's Performance and Masculinity: The Mediating Effect of Earnings Management</p>
Plagiarm A Size Face Male CEO's Performance and Masculinity: The Mediating Effect of Earnings Management
<p>Plagiarm A Size Face Male CEO's Performance and Masculinity: The Mediating Effect of Earnings Management</p>
Novel thermal management methods to improve the performance of the Li-ion batteries in high discharge current applications
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Figures for Thermal Management of the Li‐Ion Batteries to Improve the Performance of the Electric Vehicles Applications
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Raw data for "Assessing Cover Crop and Intercrop Performance Along a Farm Management Gradient" (2022)
<p>This dataset accompanies the publication "Assessing Cover Crop and Intercrop Performance Along a Farm Management Gradient" by Stratton et al. in the journal Agriculture, Ecosystems, and Environment (2022). <a href="https://doi.org/10.1016/j.agee.2022.107925">https://doi.org/10.1016/j.agee.2022.107925</a></p> <p>METHODS:</p> <p>We conducted our experiment between May 2018 and December 2019 on 14 farms in the eastern coastal highlands region of Santa Catarina, Brazil. The mean altitude of sites was 467 m (+/- 161 m). Eastern Santa Catarina has a subtropical climatic pattern, with mean annual rainfall ranging from 1,500-1,700 mm (Wrege et al., 2012). While 2018 had typical weather patterns for the region, 2019 was a dry year, particularly during the spring months (Appendix B, Table B.1). All farms were located in the Colonial Serrana Catarinense soil microregion, one of 16 designated microregions in the state of Santa Catarina (EMBRAPA, 2004). Primary soil types in our study site are associations of dystric Cambisols and haplic Acrisols (typic Dystrocryepts and typic Paleudults in the USDA Soil Taxonomy), which tend to be moderately to highly acidic, with limited soil nutrient availability and moisture retention (EMBRAPA, 2004; IUSS Working Group WRB, 2015; USDA, 2010). To support crop production, farmers in the region typically apply lime (calcium and magnesium carbonate) to agricultural fields to increase soil pH from <5.5 to 6 (Comissão de Química e Fertilidade do Solo - RS/SC, 2016). Exact farm locations within the region are not given and farmer identities have been anonymized.</p> <p><em>Experimental design</em></p> <p>The fully factorial experiment had six treatments (Figure 2): (1) cover crop + pea-cucumber intercrop, (2) cover crop + pea monocrop, (3) cover crop + cucumber monocrop, (4) fallow + pea-cucumber intercrop, (5) fallow + pea monocrop, and (6) fallow + cucumber monocrop. Due to the timing of farm recruitment, only conventional and transitioning farms participated in the first year of cover cropping (2018); agroecological farms were added to the study during the vegetable intercropping period of 2018 and had their first round of cover cropping in 2019. The cover crop mixture treatment was designed to emulate traditional practices in the region, as well as to include functionally complementary legume and grass species: common vetch (<em>Vicia sativa </em>L.) and black oat (<em>Avena strigosa </em>Schreb). We also selected vegetables with distinct ecological functional traits, such that intercropping represented an increase in functional diversity relative to mono-cropped vegetables. Snow peas are N-fixing legumes with a vining, upright structure and a deep root system, whereas cucumbers are low-lying, non-legume cucurbits that provide groundcover and have a relatively shallow, extensive root system.</p> <p> </p> <p>Cover crop treatments consisted of two adjacent 50 m2 plots in each field, one of which was planted with the cover crop mixture; the other served as a weedy fallow control. In 2018, the cover crop mixture seeding rate was 72 kg/ha black oat and 36 kg/ha common vetch. Due to poor vetch performance in mixtures at this rate, we increased the vetch seeding rate to 60 kg/ha in 2019, maintaining the black oat rate from 2018. Cover crop seeds were inoculated with the Brazilian strain <em>Rhizobium etli </em>(SEMIA 384; source: FEPAGRO) at 4 g/kg vetch seed prior to planting. Cover crops were grown until peak flowering, and then cover crops (and weeds in the fallow) were incorporated into the soil by rototiller (<em>n</em> = 7 farms) or by hand hoeing (<em>n</em> = 7 farms), based on farms’ available machinery, between September 5-10 in 2018 and September 10-18 in 2019 (approximately one week following cover crop sampling on each farm).</p> <p> </p> <p>Vegetables were planted two weeks following cover crop and weed biomass incorporation within a period of 7-10 days across sites. Harvest dates were spaced such that crops were growing for approximately the same period across farms. The 50 m2 plots were each divided into three intercrop treatments with a ~1 m2 pathway between each treatment, for a total of 6 treatments randomly assigned to plots per 100 m2. We planted a climbing variety of snow peas (<em>Pisum sativum</em> subsp. <em>sativum</em> var. <em>macrocarpum</em>,<strong> </strong>“<em>Torta de flor roxa”</em>) and pickling cucumber (<em>Cucumis sativa </em>L. var. <em>Pepino HT </em>05) in intercrops and in their respective monocrops, using a replacement design (i.e., equivalent crop densities in all treatments). Snow pea seeds were inoculated with <em>Rhizobium leguminosarum</em> var. <em>viceae</em> (SEMIA 3007/BR 619, source: UFSC ENR/CCA) at a rate of 4 g/kg directly prior to planting. There were five rows of crops per treatment, with only the three middle rows harvested to limit edge effects. In-row spacing was 60 cm for cucumber and 20 cm for peas, with 60 cm between rows in both intercrops and monocrops. Cucumbers were grown as starts for 2.5 weeks before planting, and peas were planted from seed on the same planting date as cucumber starts.</p> <p> </p> <p>In the summer between January and May 2019 all fields were planted to a sunflower (<em>Helianthus annuus</em> L.) crop, which was incorporated into the soil during flowering approximately two weeks prior to cover crop planting in 2019. Because we sought to understand the effects of crop diversification given existing water and nutrient limitations on working farms, the experiment was entirely rainfed and legume N fixation was the sole external N source.</p> <p><em>Soil sampling and analysis</em></p> <p>Prior to the first cover cropping period, we collected a composite sample of 15-20 soil cores (2.5 cm diameter, 20 cm depth) on both the cover crop and fallow sides of each experimental field (<em>n</em> = 28) for analysis of baseline conditions (see Appendix B for full details). Briefly, soil was analyzed for pH, macro- and micronutrients, and soil organic matter (SOM) by the Santa Catarina State Agricultural Agency (EPAGRI) in Ituporanga, Santa Catarina, Brazil, using standard protocols (Comissão de Química e Fertilidade do Solo - RS/SC, 2016). pH was measured with a glass electrode both with and without Sikora’s buffer, and buffered pH is used throughout this paper (Tecnal TEC-11 MP). Soil organic C and total soil N to 20 cm were determined by dry combustion on a Leco TruMac CN Analyzer (Leco Corporation, St. Joseph, Michigan, USA). We measured soil texture (% clay, sand, and silt) using a total dispersion method with sodium hexametaphosphate (Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), 1997). Bulk density was estimated from the mass of 10 fresh soil cores per treatment, with subsequent accounting for soil moisture.</p> <p> </p> <p>We measured C mineralization as a baseline indicator of soil microbial activity and biological soil fertility at the start of the experiment, and N mineralization as a response variable following the second year of cover crop treatments. Specifically, using the baseline soil sample, we conducted a short-term (24-hour) C mineralization assay to determine potentially mineralizable C (PMC), which measures the flux of CO2 following re-wetting of previously air-dried, sieved soil using a Li-Cor (Franzluebbers et al., 2000; Hurisso et al., 2016). To measure potentially mineralizable N (PMN), we conducted a two-week aerobic incubation using fresh soil collected at vegetable crop planting in the second year of the experiment (spring 2019), two weeks after cover crop and weed biomass incorporation (Drinkwater et al., 1996; Appendix B.2). PMN was calculated as the difference between extractable soil inorganic N (NH4+ and NO3-) at the start and end of the incubation. We used pre-incubation extractable inorganic N concentration (mg/kg) as a measure of soil inorganic N availability at vegetable crop planting.</p> <p> <em>Cover crop sampling and analysis</em></p> <p>Cover crop biomass sampling took place from August 28-September 2 in 2018 and September 4-10 in 2019. During peak flowering of both common vetch and black oat, we destructively harvested the aboveground biomass of cover crop mixtures and weedy fallows from two 0.5 x 0.5 m quadrats of each treatment per field. We took care to avoid treatment edges, cut plant material to the soil surface, and separated harvested plant material by species, grouping all weeds together. Aboveground biomass was dried in a forced-air oven at 60 °C for 48 hours. Following grinding in a Wiley mill to 2 mm, % N and C content was determined by dry combustion on an elemental analyzer (Leco, as above). Community-weighted means were calculated for total aboveground biomass C and N in cover crop species and weeds, to determine the overall C and N inputs to soil following incorporation of biomass on each farm. We measured biological N2 fixation in inoculated common vetch from the cover crop phase of the experiment in 2018 and 2019. Vetch N fixation was estimated using the 15N natural abundance method (Shearer and Kohl, 1986), which compares stable N isotope ratios in the legume and reference species (oat monocultures) (Appendix C).</p> <p> <em>Vegetable crop sampling and analysis</em></p> <p>To capture the full production period of both cucumber and pea crops, yield was measured in two harvests, which were approximately 14 days apart on each farm. Harvest dates ran from November 16-December 6 in 2018 and November 20-December 4 in 2019. We measured yield by weighing all harvestable fruit from three designated, representative row sections (6 plants on average per row) per crop type per treatment. Rows were sampled from the center of each treatment to reduce edge effects. We calculated yield as total crop production (g) per plant harvested in each row. Mean yield for each crop type was calculated as the average of the three harvested rows per treatment on a per-plant basis and was then aggregated to the plot and hectare level based on experimental planting densities. Total N harvested, or “N yield”, was calculated for all treatments by multiplying the % N in each vegetable crop by its yield (kg/ha) after accounting for crop water content. Using plot-level yield data, we subsequently calculated the relative yield total (Land Equivalent Ratio, LER) for intercrop treatments by farm using the standard equation (Vandermeer, 1989) (Table 1). As a relative measure of total crop production per area, when mean LER > 1, intercrops were considered to have “overyielded” compared to their component monocrops. We calculated the LER for N yield (LERN in kg N/ha) using the same formula.</p> <p> </p> <p>At the second vegetable harvest, we destructively sampled whole aboveground crop biomass, including residues and remaining fruits, from the designated experimental rows. Following the harvest, a minimum of six representative cucumbers per treatment (from different plants) per farm were washed in deionized water, air-dried, sliced, and the middle sections were combined into a homogenized, composite sample of ~100 g and then dried for one week at 60 ºC. All peas from each treatment’s subplot were washed in deionized water, air-dried, de-stemmed, chopped, and each homogenized sample (35-60 g fresh material) was subsequently dried at 60 ºC in a forced-air oven for 48 h to one week, until fully desiccated. Dried vegetable biomass residues were ground using a Wiley mill; vegetable crop samples were ground in a coffee grinder; and all vegetable samples were analyzed for % C and N on a LECO elemental analyzer.</p> <p><strong>See</strong><strong> supplemental material from Stratton et al. 2022 for further detailed information on methods.</strong></p>
RISK MANAGEMENT AND PERFORMANCE OF ROAD BUSANZA -MUYANGE CONSTRUCTION PROJECT IMPLEMENTED BY NYARUTARAMA PROPERTY DEVELOPER (NPD LTD) IN KICUKIRO DISTRICT
<div> <p><span>The study was conducted on Risk management and performance of Construction projects implemented by NPD Ltd, the study specifically will be conducted in NPD Ltd, the target population was <span>600</span> employes and <span> </span>sample size was undertaken to be <span>86</span> respondents </span><span>Study used<span> </span>descriptive statistics and inferential statistics, both qualitative and quantitative were used to analyse the data with assistance of SPSS software program version 25.0 ,</span><span>descriptive statistics and inferential statistics were used to present frequencies tables, percentages, mean and standard deviation and Inferential analysis was used in order to use Pearson correlation and multiple regression model to test the relationship between the independent variables and dependent variable.</span><span>Descriptive statistics to produce frequency tables, percentages and mean. Inferential statistics were used in order to give person correlation and multiple regression models to test and draw relationship between variables both for independent band dependent, the relationship between risk planning and performance of construction projects implemented by NPD Ltd in Kicukiro district.</span><span> The overall means</span><span> of results was <span>4.50,</span></span><span> the relationship between risk analysis and performance of construction projects implemented by NPD Ltd in Kicukiro district w</span><span>as <span>4.50,</span></span><span> the relationship between risk analysis and performance of construction projects implemented by NPD Ltd in Kicukiro district.</span><span> The overall means</span><span> of results was<span> 4.60,</span></span><span> the relationship between risk response and performance of construction projects implemented</span><span> by NPD Ltd in Kicukiro district.</span><span> The overall means</span><span> of results was<span> 4.49,</span></span><span> </span><span>to examine the relationship between risk mitigation and performance of construction projects</span><span> implemented by NPD Ltd in Kicukiro district.</span><span> The overall means</span><span> of results was <span>4.27 and </span>the data on </span><span>performance of project </span><span>was</span><span> analyzed, the overall means of results was </span><span>4.52</span><span>.</span><span> Depending on the results, it presented that the performance of project was on good grades. It indicated that the relationship between risk management and performance of project on road construction between risk planning, risk analysis, risk response and risk mitigation and performance was <span>0.617, 0.855 ,0<sup> </sup>.867and 0.827</span> </span><span>respectively, and the results presented than the variables were statistically significant with p value=0.000b</span><span>.</span><span> The results present the variables of Risk management; <span>Risk Planning, </span>was not statistically significant with p value=0.479<sup>b</sup>, the <span>Risk Analysis</span> was statistically significant with p value=0.010<sup>b</sup>, and the <span>Risk Response</span> was statistically significant with p value=0.000<sup>b</sup>. And the <span>Risk Mitigation</span> was statistically significant with p value=0.000<sup>b</sup>. </span><span>It concluded that there was a significant relationship between risk management and performance of project. The study recommended that</span><span><span> MINFRA should</span></span><span> mobilize Rwandans to</span><span> be involved in construction activities in order to earn money as the sector engage many labors. </span></p> </div> <p><strong><span> </span></strong></p> <p><strong><span> </span></strong></p> <p><strong><span>Keywords: </span></strong><span>Risk management, performance, Road construction project and implementation </span></p>
Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat
<p>Highly mobile species, such as migratory birds, respond to seasonal and inter-annual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally-resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16-days), and 17-year long-term average, surface water data. Using abundance-weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. While modeling with both real-time and long-term average data provided good fit to withheld validation data (0.79 < AUC < 0.89 across taxa), there were small differences in model performance. The best models incorporated long-term average conditions and spatial pattern information for real-time flooding (e.g. perimeter-area ratio of real-time water bodies). There was not a substantial difference in the performance of real-time and long-term average data models within time periods when real-time surface water differed substantially from the long-term average (specifically during drought years 2013-2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real-time data may ultimately be best for guiding dynamic, adaptive conservation actions whereas models based on long-term averages may be more helpful for guiding permanent wetland protection and restoration. --</p>
Impact of the Introduction of a Performance Improvement Program on the Initial Management of Sepsis and Septic Shock in Adults in the Emergency Department: a Before-and-after Study (IPA-SOS)
ClinicalTrials.gov study NCT06657625. IPD Sharing: YES. Countries: 1. Publications: 5.
Trial of Multidisciplinary Team Stress and Performance in Immersive Simulation for Management of Infant in Shock
ClinicalTrials.gov study NCT02424890. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Automated Diabetes Registry Tools to Enhance Patient Self-Management and Provider Performance Feedback
ClinicalTrials.gov study NCT00827710. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Paternalistic Leadership Training Given to Health Managers on Nurses' Motivation, Organizational Commitment, and Performance
ClinicalTrials.gov study NCT06131307. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Comparison Ultrasound-Guided Adductor Canal Block and Surgeon-Performed Block for Pain Management After Total Knee Arthroplasty
ClinicalTrials.gov study NCT06533085. IPD Sharing: NO. Countries: 1. Publications: 1.
Transanal Inspection and Management of Low ColoRectal Anastomosis Performed With a New Technique
ClinicalTrials.gov study NCT02879370. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.
Oral Dexamethasone for the Management of Symptomatic Irreversible Pulpitis Without Performing Pulpotomy
ClinicalTrials.gov study NCT05761730. IPD Sharing: NO. Countries: 1. Publications: 4.
Performance Evaluation of Clinical Ultrasound in Management of Acute Pulmonary Edema in Elderly Patient
ClinicalTrials.gov study NCT02638350. IPD Sharing: NO. Countries: 1. Publications: 5.
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