Statistical methods for sensitivity and performance analysis in computer experiments

Airway management is a critical intervention for patients with airway compromise, respiratory failure, and cardiac arrest. Many EMS agencies use drug-assisted airway management (DAAM) - the administration of sedatives alone or in combination with neuromuscular blockers - to facilitate advanced airway placement in patients with airway compromise or impending respiratory failure who also have altered mental status, agitation, or intact protective airway reflexes. While DAAM provides several benefits including improving laryngoscopy and making insertion of endotracheal tubes and supraglottic airways easier, DAAM also carries important risks. NAEMSP recommends: DAAM is an appropriate tool for EMS clinicians in systems with clear guidelines, sufficient training, and close EMS physician oversight. DAAM should not be used in settings without adequate resources. EMS physicians should develop clinical guidelines informed by evidence and oversee the training and credentialing for safe and effective DAAM. DAAM programs should include best practices of airway management including patient selection, assessmenct and positioning, preoxygenation strategies including apneic oxygenation, monitoring and management of physiologic abnormalities, selection of medications, post-intubation analgesia and sedation, equipment selection, airway confirmation and monitoring, and rescue airway techniques. Post-DAAM airway placement must be confirmed and continually monitored with waveform capnography. EMS clinicians must have the necessary equipment and training to manage patients with failed DAAM, including bag mask ventilation, supraglottic airway devices and surgical airway approaches. Continuous quality improvement for DAAM must include assessment of individual and aggregate performance metrics. Where available for review, continuous physiologic recordings (vital signs, pulse oximetry, and capnography), audio and video recordings, and assessment of patient outcomes should be part of DAAM continuous quality improvement.

Statistical and experimental methods in sensitivity analysis

In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (TT), k-NN20 (k-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (PS), DTR (decision tree regressor) ranks first with average and median values of 5.6784×106 observations per second (ob/s) and 4.8691×106 observations per second (ob/s), and 4.9929×106 observations per second (ob/s) and 5.8806×106 observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (R2), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (MSE), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. DTR and k-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.

There are previous studies in performance analysis that have concluded significant effects by using a large number of events from a small number of matches. The statistical tests applied in such studies are inappropriate for non-independent data and significant results might not be achieved if performance indicators from whole performances were used. This paper illustrates the extent of the problem using an example where significant results are achieved using individual events from a sample of 9 matches. When the unit of analysis was a match rather than an event, no significant effects were found. Therefore, generalisations cannot be made from small numbers of matches where significance has been concluded based on inappropriate use of statistical procedures. It is recommended that performance analysis investigations use whole performances as units of analysis.

A single-model 16-member ensemble is used to investigate how external model factors can affect model performance. Ensemble members are constructed with the land surface model (LSM) Joint UK Land Environment Simulator (JULES), with different choices of meteorological forcing [in situ, NCEP Climate Forecast System Reanalysis (CFSR)/CFSv2, or Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI)] and ancillary datasets (in situ or remotely sensed), and with four time step modes. Effects of temporal averaging are investigated by comparing the hourly, daily, monthly, and seasonal ensemble performance against snow depth and water equivalent, soil temperature and moisture, and latent and sensible heat fluxes from one forest site and one clearing in the boreal ecozone of Finnish Lapland. Results show that meteorological data are the largest source of uncertainty; differences in ancillary data have little effect on model results. Although generally informative and representative, aggregated performance metrics fail to identify “right results for the wrong reasons”; to do so, scrutinizing of time series and of interactions between variables is necessary. Temporal averaging over longer intervals improves metrics—with the notable exception of bias, which increases—by reducing the effects of internal data and model variability on model response. Model evaluation during shoulder seasons (fall minus spring) identifies weaknesses in the reanalyses datasets that conventional seasonal performance (winter minus summer) neglects. In view of the importance of snow on the range of results obtained with the same model, let alone identical simulations using different temporal averaging, it is recommended that systematic evaluation, quantification of errors, and uncertainties in snow-covered regions be incorporated in future efforts to standardize evaluation methods of LSMs.

The objective of the paper is to define the factors that contribute to the development of the agricultural sector of Ukraine’s economy, to analyze the places Ukraine takes in the world rankings of agricultural production and export and the share of the agricultural and food industry products in the total exports volume of Ukraine, to define the main markets for Ukrainian agricultural products, to identify the role agroholdings play in the functioning of the Ukrainian agricultural sector, to define the main factors that contributed to the emergence and development of the agroholdings in Ukraine and to make the list of the most profitable agroholdings in Ukraine. The following general scientific and specific economic methods were used in the paper: the statistical analysis methods, the method of ratings, the information – computer methods, the critical analysis method, the monographic method, the case study method and the graphical method.

The observational research based on big data in healthcare has attracted increasing attention, with the control and evaluation of residual confounding being the critical issue that needs to be solved urgently. This review summarized the methods for statistical adjustment and sensitivity analysis of residual confounding in the association analysis with a multicenter database. Based on individual-level data, the residual confounding can be adjusted in each subcenter using methods such as regression discontinuity design, while the pooled estimate can be obtained as a weighted average. Based on the center-level results, the Bayesian Meta-analysis method can adjust the pooled estimates. The sensitivity analysis of residual confounding can also be carried out using center-level data to calculate the E-value, p^(q), T^(r, q) and G^r,q. The abovementioned methods should be selected reasonably according to the requirements for practical applications, advantages, and disadvantages. For example, the use of subcenter individual data for residual confounding adjustment usually needs strict study design and frequent coordination; the Bayesian Meta-analysis is based on some strong assumptions; the interpretation of the results in the sensitivity analysis, such as E-value requires professional judgment to assess the risk of residual confounding. Therefore, the methods for controlling and evaluating residual confounding in association analysis based on multicenter databases still need further development and improvement.

Introduction: The study emergency department (ED) receives a new cohort of junior doctors every 6 months as part of the national manpower allocation to meet health service needs. An approach to effect a consistent reduction in prescribing errors by these doctors during their 6-month postings at the study ED was introduced in May 2009. Methods: Monthly broadcasting of prescribing error counts by individual doctors allowed performances to be openly benchmarked against one another while providing a reference for self-improvement. The monthly tally of absolute counts were computed into a pre-determined formula and translated into monetary rewards for the junior doctors. Control charts of total count per month, mean count per doctor per month and highest individual count per month were plotted based on a total of 48 data points for 8 cohorts over 4 years from May 2007 to April 2011. Results: There was a shift towards fewer absolute counts and lower mean count of prescribing errors per month in the post-intervention phase, suggesting a positive impact of the intervention on the doctors as a cohort. The intervention's impact on individual performances, however, was less convincing with minimal change in variation in the highest individual count per month over the 4 years. Conclusion: The use of metrics has helped to drive and sustain a reduction in prescribing errors among junior doctors as a cohort during their 6-month postings in the study ED.

Resistive Random Access Memory (ReRAM) is a novel form of non-volatile memory with a variety of applications ranging from neuromorphic circuits used for artificial intelligence applications, to a potential replacement for flash memory. Each application has specific requirements including Roff/Ron ratio, maximum variability tolerance, retention, endurance, and operating voltages. Tuning of individual performance metrics for a particular application often results in performance trade-offs. In this work, we demonstrate the adjustment of the above-mentioned parameters via manipulation of the pre-forming oxygen vacancy gradient using a controlled annealing step with nanoscale HfO2-based ReRAM devices. ReRAM devices of 100 × 100 nm2 were implemented between the metal 1 and 2 interconnect in a custom designed split via process, using 65 nm CMOS processing technology. A temperature study on these devices was performed to optimize the oxygen vacancy gradient from the switching layer to the oxygen scavenger layer. Devices in a 1 transistor/1 ReRAM (1T1R) configuration were subjected to 300 °C–400 °C, for 1–120 min and then electrically characterized using DC switching and endurance testing. Based on the results of this study, we outline the temperature constraints for post-fabrication drive-in of oxygen vacancies via annealing-based treatment of HfO2 based ReRAM devices.

Interseeding, the practice of planting cover crops into standing cash crops, can enhance soil health, nutrient cycling, and weed suppression, although performance depends on species and environmental context. We evaluated three cover crop mixtures with potential for use in the Northeast United States: (i) oats ( Avena sativa ) and field peas ( Pisum sativum ) (OP); (ii) cereal rye ( Secale cereale ) and hairy vetch ( Vicia villosa ) (WRV); and (iii) annual ryegrass ( Lolium multiflorum ) and crimson clover ( Trifolium incarnatum ) (ARGCL). These were interseeded in two separate trials: (i) sweet corn ( Zea mays ) and (ii) storage cabbage ( Brassica oleracea ) over two growing seasons. Both trials included a no-cover-crop control for comparison. Cover crop biomass, weed biomass, soil nitrate, soil water dynamics, and cash crop yields were measured, and an integrated suitability score of the cover crops was calculated to compare overall performance. The results indicated cover species- and environmentally dependent variation in cover crop biomass. OP frequently ranked highest in the integrated suitability assessment across years and in both cash crops, although individual performance metrics were often not significantly different among treatments. ARGCL and WRV also demonstrated high suitability scores for sweet corn in 2022 environmental conditions. Surprisingly, weed biomass, soil nitrate, and soil water content did not differ among cover crop species, indicating that interseeded cover crops did not substantially alter these measures. Cash crop yields were generally maintained across treatments, although in 2024, cabbage yields in the ARGCL plots were reduced. These findings suggest that, under the conditions of this study, carefully selecting cover crop mixtures according to management and ecological setting may support expanded interseeding adoption without consistent yield penalties in Northern New England.

Over the last four decades, the bootstrap method has been considered so as to define data‐driven bandwidth selectors for nonparametric curve estimation. An extensive and updated review of bootstrap methods used to select the smoothing parameter for the nonparametric estimation of several curves has been carried out. Different data generating processes have been profoundly reviewed, such as the classical independent and identically distributed setup as well as dependent, censored, length‐biased, grouped, missing or directional data, among others. Several curves have also been considered, such as the density, regression, hazard rate, intensity, latency, incidence, or distribution functions, among many others. It is worth mentioning that there exist situations where Monte Carlo methods are not needed when using the bootstrap for bandwidth selections. This idea has also been exploited to find closed expressions for the bootstrap version of criterion functions for some proxy of the real curve estimator.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bootstrap and Resampling Statistical and Graphical Methods of Data Analysis > Nonparametric Methods Statistical and Graphical Methods of Data Analysis > Density Estimation

The methodical tools development of a complex sociological research of the population activity assessment results based on residents survey is the important mechanism of the analysis of socio-economic development conditions, strategic interests and scenarios of municipal unit. Definition of objective assessment of the population activity on the municipal formation territory “Staromajnskij district” of the Ulyanovsk region for practical actions of local governments based on the sociological research results “How is living in the Staromajnskij district” is an objective of this research. General scientific methods of research: questioning, system analysis, comparison and analogy method, generalization method, statistical analysis methods, content analysis, graphical and tabular methods of data visualization were used in the work. The organization and carrying out a sociological research of the population activity assessment of the municipal formation “Staromajnskij district” allowed to reveal the social well-being levels, life conditions, domestic territories arrangement, objects of social and cultural-consumer services, transport infrastructure, ecological situation, socio-economic problems of the Staromajnskij district, its competitiveness and development prospects. The research selection was 1100 respondents of local community, subjects of small and medium-sized business, local governments and public organizations representatives. The economic efficiency and significance of the work are due to the need to justify the system of goals, objectives and strategic directions of municipal unit development till 2030 providing its competitiveness and economic growth in the long term, as well as the formation of methodological recommendations for the organization of a work system to improve populations livelihoods.

Industrial use of natural resources are increasing at an alarming rate. Engineering and decision support tools are needed for analyzing and curbing industrial consumption of resources. Further, assessment methods to measure and indicate continuous improvement are also needed. Modeling individual manufacturing processes facilitates the generation of quantifiable evidence that improvements are being made. Such a modeling approach is developed and demonstrated in this paper to characterize sustainability performance of two metals inspection processes: magnetic particle inspection for ferromagnetic alloys and penetrant inspection for non-ferromagnetic alloys. Individual unit manufacturing process (UMP) models were developed by observing the inspection practices at an aircraft component manufacturer, and a mathematical basis for comparison with other inspection processes was identified. The paper further demonstrates the aggregation of performance metrics from all UMPs across a manufacturing process flow thus providing a basis for generating detailed sustainability performance assessments of manufactured products. By developing and documenting a comprehensive set of UMP models, more complete knowledge of manufacturing processes can be gained by industry practitioners, leading to continuous improvement of sustainability performance.

Introduction. Energy is a strategically important industry, the smooth and efficient operation of all spheres of the domestic economy depends on its safe and stable functioning. Problem Statement. The energy system of Ukraine provides the economy not only for its country, but also for the countries of the European Union. However, as a result of a full-scale Russian aggression and massive missile attacks, the systematic supply of electricity in certain locations, which took place during peacetime, is being destroyed. Purpose. To conduct the assessment of the energy system of Ukraine before the invasion and destruction due to Russian aggression, to identify the advantages of using blockchain in the energy and climate sectors to restore energy capacities. Methods. General scientific methods, statistical and economic methods of analysis, comparison, and graphic methods are used. Results. Attention is focused on the role of the energy system in the economy of Ukraine. Changes in the domestic energy system and its synchronization with the harmonized EU network are indicated. The reasons for the decrease in demand for electricity after the full-scale Russian invasion have been revealed. Factors indicating a long period of restoration of the power system have been identified. The structure of generation in Ukraine is given and its component capacities are outlined. TPPs of Ukraine and the possibility of importing lignite and hard coal from Europe to ensure their operation are characterized. Emphasis is placed on the development of renewable energy sources in Ukraine. Conclusions. It has been proven that the restoration of the power system must be carried out taking into account the latest IT technologies using the digital ecosystem. Among many digital technologies, blockchain will help support and optimize evidence-based decision-making in the areas of climate and sustainable energy. Blockchain solutions for the energy transition confirm that this technology has a high potential to be used as the “distributed engine” of the energy community, pointing to it as a tool that will radically change the energy sector.

The research is aimed at studying the state and territorial imbalances in the development of grain farming in Russia, identifying the main areas of production and processing of grain crops, as well as priority areas for the development of the subcomplex. The study was conducted using a number of methods and approaches, among which the main ones were horizontal and vertical analysis, statistical and economic methods of analysis, and the graphical method. In the development of the grain industry in Russia, territorial imbalances remain between the localization of the main grain growing and processing zones, largely related to logistical and economic factors. Thus, despite the leadership of the Krasnodar territory and the Rostov region in terms of gross grain harvests, the largest areas of the milling industry are the Moscow and Leningrad regions. The geographical location of grain growing areas is determined to the greatest extent by natural and climatic features, while locally processing of grown grain is practically not carried out. The scientific novelty lies in the assessment of the zonal location of grain production and processing with the formation of a scheme for the geographical location of production and processing zones in relation to the map of Russia.