Analysis Of Distribution Data Research Articles

Assessment of the levels of formation and leading components of the patient safety system of the anesthesiology and resuscitation service (message 2)

The objective was to assess the levels of formation and leading components of the patient safety system of the anesthesiology and resuscitation (A&R) service (department) of multidisciplinary hospitals.Materials and methods. Expert assessment of the patient safety system of the anesthesiology and resuscitation service (department) of 235 multidisciplinary hospitals, including the assessment of its level, leading components, and their structure in the overall sample. Expert assessment method. Statistical analysis of data distribution, Student’s t-test for independent samples of different sizes, correlation analysis.Results. It has been established that in the main part (38.3%) of multidisciplinary hospitals, the A&R service is at an average (sufficient) level of ensuring patient safety. 17% are at a high (organized) and highest (system-organized) level in terms of ensuring patient safety. A fairly large part (44.7%) is at a low (insufficient) and minimal (critical, extremely low) level of ensuring patient safety. At the same time, the level of ensuring patient safety in the A&R service of multidisciplinary hospitals is important and has a close but inverse relationship with the level of digitalization. Comparison of the severity of the five leading (main) components (criteria) for ensuring patient safety in the A&R service of multidisciplinary hospitals made it possible to assess the levels of their completeness and their structure and showed that in the general sample, the most pronounced criteria for ensuring patient safety were «Teamwork, staff expertise» and «Availability of medicines».Conclusion. The current state of the patient safety system of the A&R service of multidisciplinary hospitals is characterized by 5 levels and 5 leading (main) components, the expert assessment of which made it possible to distribute the A&R services of hospitals according to the indicated levels, identify the structure of their leading components an determine target areas for improving the patient safety system.

Error detection for radiotherapy planning validation based on deep learning networks.

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3mm; 3%, 2mm; 2%, 2mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Feasibility of zero emission freight zones: scenario analysis and risk assessment

To further incentivize the adoption of zero-emission trucks (ZETs), Chinese cities would need more proactive policies like zero-emission freight zones (ZEFZs). However, the design of the policy should avoid the disruption of goods supply, increase of logistic costs, and ensure inclusive transition of small carriers and financial sustainability for city governments. To this end, this study employs Beijing as a case: Based on ZEFZ case studies, policy document and literature review, and data analysis of freight demand distribution and truck movements in Beijing, this study constructs four scenarios with different spatial coverages, restricted truck segments, and roll-out years of ZEFZs. The impacts of different ZEFZ scenarios on emissions reduction potentials, goods supply and vehicle fleet replacement, and public and private expenditure are evaluated. The most cost-efficient and easy-to-implement scenario is recommended as the initial phase of the ZEFZ scheme, with risk assessment and risk mitigation measures.

Solvent Extraction of Gold(III) by 2-Ethylhexanol and Modeling of Facilitated Transport across a Supported Liquid Membrane

The solvent extraction of gold(III) by undiluted 2-ethylhexanol or dissolved in toluene from a HCl solution has been investigated in this paper. The numerical analysis of gold distribution data suggests the formation of HAuCl4·L and HAuCl4·2L (L = 2-ethylhexanol) species in the organic phase, with formation constants K11 = 38 and K12 = 309, respectively. The results derived from gold(III) distribution have been implemented in a solid-supported liquid membrane system. The influence of several variables on gold transport has been considered: feed and receiving phases’ stirring speeds, HCl and gold concentrations in the feed phase, and carrier concentration in the membrane phase as well as the presence of base metals (Fe, Cu, Ni) and platinum-group metals (PGMs) in the feed phase. Gold transport is influenced by the stirring speed of the feed phase and the variation in HCl and gold (feed phase) and carrier (membrane phase) concentrations. Also, diffusional resistances to mass transfer are estimated, and the contribution of each resistance to gold transport is estimated. Gold is recovered as zero-valent nanoparticles.

Data-driven determination of zooplankton bioregions and robustness analysis

Identifying biogeographic regions through cluster analysis of species distribution data is a common method for partitioning ecosystems. Selecting the appropriate cluster analysis method requires a comparison of multiple algorithms. In this study, we demonstrate a data-driven process to select a method for bioregionalization based on community data and test its robustness to data variability following these steps:•We aggregated and curated zooplankton community observations from expeditions in the Northeast Pacific.•We determined the best bioregionalization approach by comparing nine cluster analysis methods using ten goodness of clustering indices.•We evaluated the robustness of the bioregionalization to different sources of sampling and taxonomic variability by comparing the bioregionalization of the overall dataset with bioregionalizations of subsets of the data.The K-means clustering of the log-chord transformed abundance was selected as the optimal method for bioregionalization of the zooplankton dataset. This clustering resulted in the emergence of four bioregions along the cross-shelf gradient: the Offshore, Deep Shelf, Nearshore, and Deep Fjord bioregions. The robustness analyses demonstrated that the bioregionalization was consistent despite variability in the spatial and temporal frequency of sampling, sampling methodology, and taxonomic coverage.

Meta Data Analysis of Sex Distribution of Study Samples Reported in Summer Biomechanics, Bioengineering, and Biotransport Annual Conference Abstracts.

The biased use of male subjects in biomedical research has created limitations, underscoring the importance of including women to enhance the outcomes of evidence-based medicine and to promote human health. While federal policies (e.g., the 1993 Revitalization Act and the 2016 Sex as a Biological Variable Act) have aimed to improve sex balance in studies funded by the National Institutes of Health (NIH), data on sex inclusivity in non-NIH funded research remain limited. The objective of this study was to analyze the trend of sex inclusion in abstracts submitted to the Summer Biomechanics, Bioengineering, & Biotransport Conference (SB3C) over 7 years. We scored every abstract accepted to SB3C, and the findings revealed that approximately 20% of total abstracts included sex-related information, and this trend remained stable. Surprisingly, there was no significant increase in abstracts, including both sexes and those with balanced female and male samples. The proportion of abstracts with balanced sexes was notably lower than those including both sexes. Additionally, we examined whether the exclusion of one sex from the corresponding studies was justified by the research questions. Female-only studies had a 50% justification rate, while male-only studies had only 2% justification. Disparity in sex inclusion in SB3C abstracts was apparent, prompting us to encourage scientists to be more mindful of the sex of the research samples. Addressing sex inclusivity in biomechanics and mechanobiology research is essential for advancing medical knowledge and for promoting better healthcare outcomes for everyone.

Can redistribution of vaccine improve global welfare? Lessons from COVID-19.

The disparity in the distribution of COVID-19 vaccine has been recorded with more than 70% vaccination rate for high-income countries as compared to less than 40% for low-income countries. The low affordability of vaccines for the majority of low-income group invites the need for redistribution of vaccines. The disproportionate donation of vaccines across the globe motivates us to explore the incentive for the high-income group to redistribute. An exploratory analysis of cross-country COVID-19 vaccination distribution data shows that the countries which have received vaccines as donation has also contributed to vaccine wastage. This paper intends to provide a theoretical background for this counterintuitive observation using welfare analysis. We find that the market mechanism leads to a negative impact on global welfare due to redistribution. Only an invention with a defined redistribution mechanism may ensure an increase in global welfare. It is found that a critical value of redistribution mechanism reinforced by a minimum threshold level of income is essential to enhance welfare. The reduced form from the theoretical predictions is empirically validated with cross-country data on COVID-19 vaccination for all countries. As identified in theory, the internal support variables like political stability, government effectiveness, and health expenditure at the country level will impact global welfare. Therefore, when global cooperation is essential during a health crisis like COVID-19, improved internal coordination and intentions cannot be ignored.

Optimized Design of Scoring Criteria for Large-Scale Innovation Competitions

The reasonable and reliable evaluation score calculation scheme wins the top priority in large-scale innovation-competitions currently. Due to the disparity among individual experts, and the existing standard score calculation model merely based on the experts' own circumstance, it cannot fully reflect the comprehensive level of players, resulting in a certain degree of error in the evaluation results. From the perspective of individual and decision-making of a group, this paper improves existing models and introduces the concept of modified scores. To verify the feasibility of the model, four schemes were designed based on the analysis of data distribution. Consistency and difference factors were applied to compare. Come to a conclusion: scheme adopted the new standard score calculation model ranked the top, indicating that the new calculation model is more reliable. Next, considering that the volatile correction factor affected by data, there is a possibility of some poor actual results with high correction scores. To rectify this case, a power exponent is added to the correction factor and a sign function is adopted to specify the positive or negative of correction scores. To verify the feasibility of the model, four sets of controlled experiments were designed with the introduction of two factors: power exponent and reconsideration bonus, as well as the ranking consistency test based on the condition that the award order of the expert agreement was consistent. In the end, it was found that the scheme that introduced both power exponent and reconsideration bonus points had a sorting consistency rate of 74%, which increased by 30% compared to the original model, indicating the rationality of the modification. The expert evaluation standard score calculation model established in this paper comprehensively considers individual and group decision-making, and provides reasonable correction for the differential scoring between experts. At the same time, this scheme provides a reference basis for further optimizing large-scale innovation competition plans in the future.

Spatio-Temporal Variability of the Aerosol Optical Depth over the Gorky and Cheboksary Reservoirs in 2022–2023

The present study aimed to investigate atmospheric optical characteristics over the Gorky and Cheboksary Reservoirs and their multi-scale temporal variations to obtain the background characteristics and to identify events involving the transfer of absorbing aerosol to the studied region in 2022–2023. The region is located at a considerable distance (500 km) from the nearest AERONET station; therefore, previous atmospheric data were not available. As a solution, the in situ self-measured aerosol optical depth (AOD) and Angström exponent, as well as satellite products (MAIAC and CALIPSO) for MODIS data, were used. This allowed us to set background values of an AOD of 0.11 at a wavelength of 500 nm and an Angström exponent of 1.2, against their maximum values of 0.38 and 2.5, respectively. To explain these variations, the registered conditions and the microstructure of the dust aerosol over the studied region are presented. For days with background values, the analysis of the particle size distribution data did not show a predominance of any particle size. The optical properties of a smoke aerosol in an atmospheric column are described, and an analysis of the dynamics of particle size variability is presented. A comparative analysis of the optical characteristics of atmospheric aerosol over the Gorky and Cheboksary Reservoirs using in situ and MODIS products was carried out.

Enhanced harvest performance predictability through advanced multivariate data analysis of mammalian cell culture particle size distribution.

The industry's pursuit for higher antibody production has led to increased cell density cultures that impact the performance of subsequent product recovery steps. This increase in cell concentration has highlighted the critical role of solids concentration in centrifugation yield, while recent product degradation cases have shed light on the impact of cell lysis on product quality. Current methods for measuring solids concentration and cell lysis are not suited for early-stage high-throughput experimentation, which means that these cell culture outputs are not well characterized in early process development. This article describes a novel approach that leveraged the data from a widely-used automated cell counter (Vi-CELL™ XR) to accurately predict solids concentration and a common cell lysis indicator represented as lactate dehydrogenase (LDH) release. For this purpose, partial least squares (PLS) models were derived with k-fold cross-validation from the particle size distribution data generated by the cell counter. The PLS models showed good predictive potential for both LDH release and solids concentration. This novel approach reduced the time required for evaluating the solids concentration and LDH for a typical high-throughput cell culture system (with 48 bioreactors in parallel) from around 7 h down to a few minutes.

Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets

The widespread adoption of photovoltaic (PV) technology for renewable energy necessitates accurate segmentation of PV panels to estimate installation capacity. However, achieving highly efficient and precise segmentation methods remains a pressing challenge. Recent advancements in artificial intelligence and remote sensing techniques have shown promise in PV segmentation. Nevertheless, real-world scenarios introduce complexities such as diverse sensing platforms, sensors, panel categories, and testing regions. These factors contribute to resolution, size, and foreground-background class imbalances, impeding accurate and generalized PV panel segmentation over large areas. To address these challenges, we propose GenPV, a deep learning model that leverages data distribution analysis and PV panel characteristics to enhance segmentation accuracy and generalization. GenPV employs a multi-scale feature learning approach, utilizing an enhanced feature pyramid network to fuse data features from multiple resolutions, effectively addressing resolution imbalance. Moreover, inductive learning is employed through a multitask approach, facilitating the detection and identification of both small and large-sized PV panels to mitigate size imbalance. To address significant class imbalance in PV panel recognition tasks, we integrate the Focal loss function for effective hard sample mining. Through experimental evaluation conducted in Heilbronn, Germany, our proposed method demonstrates superior performance compared to state-of-the-art approaches in PV panel segmentation. The results exhibit progressively higher accuracy and improved generalization capability. These findings highlight the potential of our method to serve as an advanced and practical tool for PV segmentation in the renewable energy field.

A Study on Wheel Member Condition Recognition Using Machine Learning (Support Vector Machine).

The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the operation of railway vehicles. To address this issue, this paper proposes a method for predicting abnormalities in railway wheels in advance and enhancing the learning and prediction performance of machine learning algorithms. Data were collected during the operation of Line 4 of the Busan Metro in South Korea by directly attaching sensors to the railway vehicles. Through the analysis of key factors in the collected data, factors that can be used for tire condition classification were derived. Additionally, through data distribution analysis and correlation analysis, factors for classifying tire conditions were identified. As a result, it was determined that the z-axis of acceleration has a significant impact, and machine learning techniques such as SVM (Linear Kernel, RBF Kernel) and Random Forest were utilized based on acceleration data to classify tire conditions into in-service and defective states. The SVM (Linear Kernel) yielded the highest recognition rate at 98.70%.

Comparative analysis of energy-efficient air conditioner based on brand

The availability of numerous air conditioners in the market with various brands and types often leads consumers to be unaware that the purchased air conditioner may be inefficient in terms of energy usage. This research aims to determine the most energy-efficient air conditioner based on the brand of air conditioners available in the market. The research method consists of four stages: data collection, data preprocessing, data analysis, and interpretation of results and conclusions. The data used in this study was obtained from the database of the Directorate General of New, Renewable, and Energy Conservation (EBETKE), which consists of 11 AC brands sold in the market. Data analysis was performed using data distribution analysis techniques, standard deviation calculations, and correlation analysis between variables, such as the Pearson's correlation coefficient. The results of this study show that the AC brand with the highest average efficiency value is Mitsubishi Electric, with a value of 16.36 Energy Efficiency Ratio (EER), while the AC brand with the lowest average efficiency value is GREE, with a value of 5.640 (EER). Each AC brand has a different average efficiency value, with significant variations. From the correlation heatmap results, the AC power does not appear to significantly affect the AC efficiency value, where AC with lower power tends to have higher efficiency values, but there are also AC with high power and high efficiency values. Additionally, the cooling capacity value also appears to have a small effect on the AC efficiency value, where AC with lower cooling capacity tends to have higher efficiency values. However, some AC brands have high cooling capacity values but also have high efficiency values. This study also shows a moderate correlation between the AC efficiency value and the AC's annual energy consumption value, where AC with higher efficiency values tends to have lower annual energy consumption values.

Data analysis of compositional distribution and glass transition temperature of low-melting phosphate glass using big data

Data analysis of compositional distribution and glass transition temperature of low-melting phosphate glass using big data

3-D Indoor Positioning Based on Passive Radio Frequency Signal Strength Distribution

In recent years, indoor positioning systems (IPSs) have received attention from many research fields, such as robotics, navigation, human-computer interaction, etc. However, IPS based on passive radio frequency (PRF) technology is still rare. This paper proposes an three-dimensional (3D) IPS based on received signal strength (RSS) distribution and Gaussian process regression (GPR). Traditional RSS-based positioning systems have a transmitter with known frequencies, while in the proposed PRf signal of Opportunity -3D IPS (PRO-3DIPS), the system neither deploys new transmitters nor uses any a priori knowledge of transmitters. Furthermore, PRO-3DIPS integrates multiple Signal of Opportunity (SoOP) sources, shadowing, fading, and also captures scenario signatures. Data collection and analysis of PRF-based RSS distribution in 3D space enables the capability of 3D positioning. Three methods are applied and compared to find the frequency band most impacted by the scenario to achieve the best positioning performance as well as used in the estimation of RSS distribution. The RSS distribution is estimated by measuring the PRF spectrum on a fixed grid in the scenario. Using the RSS distribution, the GPR can accurately locate the receiver position. RSS at 90-gridded positions were collected in the experiment scenario, with one hundred samples at each position. The experimental result shows that a root mean square error (RMSE) of the proposed PRO-3DIPS is 0.292 meters when the sampling distance is 1 meter. The result demonstrates that the PRF spectrum is a new modality for the positioning task, which demonstrates better performance than most existing RF-based technologies.

Calidad de servicio y satisfacción del usuario de telefonía móvil en una Universidad Peruana

The objective of this study was to determine the relationship between service quality and mobile phone user satisfaction at a Peruvian University. The type of research has a quantitative approach with a non-experimental cross-sectional design. The sample consisted of 149 students of the institution applying a questionnaire of 36 questions with a Likert scale. Likewise, an internal consistency test was carried out through KMO and Bartlett with a significance of 0.00, which indicates that the instrument is valid and the Cronbach's alpha test, yielding a reliability result of 0.958. For the analysis of data distribution under the normality test, the Kolmogorov-Smirnov method was used with a significance level of 0.00, this determines that the distribution is not normal. It is concluded that there is a significant correlation between the variables quality of service and user satisfaction with a p value of 0.00, likewise a Kendall Tau-b correlation coefficient of 0.881 was evidenced, showing a very good and significant correlation between both variables.

Assumptions for Structure Equation Modeling (SEM), Normality of Data Distribution Analysis & Model Fit Measures

Assumptions for Structure Equation Modeling (SEM), Normality of Data Distribution Analysis & Model Fit Measures

Data distribution analysis – a preliminary approach to quantitative data in biomedical research

Statistical analysis is an integral part of medical research. It helps transform raw data into meaningful insights, supports hypothesis testing, optimises study design, assesses risk and prognosis, and facilitates evidence-based decision-making. The statistical analysis increases research findings' reliability, validity and generalisability, ultimately advancing medical knowledge and improving patient care. Without it, meaningful analysis of the data collected would be impossible. The conclusions drawn would be unsubstantiated and misleading. Many health professionals are unfamiliar with statistical analysis and its basic concepts. The analysis of clinical data is an integral part of medical research. Identifying the data type (continuous, quasi-continuous or discrete) and detecting outliers are the first and most important steps. When analysing the data distribution for normality, graphical and numerical methods are recommended. Depending on the type of data distribution, appropriate non-parametric or parametric tests can be used for further analysis. Data that are not normally distributed can be normalised using various mathematical methods (e.g., square root or logarithm) and analysed using parametric tests in the next steps. This review provides essential explanations of these concepts without using complex mathematical or statistical equations but with several graphical examples of various statistical terms.

Secondary Data Analysis of Indonesian Doctors Distribution In 2021

Secondary Data Analysis of Indonesian Doctors Distribution In 2021

THE APPLICATION OF THE PROBLEM-BASED LEARNING TEACHING MODEL IN IMPROVING STUDENTS' ABILITY TO SOLVE HIGHER ORDER THINKING SKILLS QUESTIONS

This study aimed to determine the increase in the ability to solve Higher Order Thinking Skills (HOTS) questions in the economics subject. This quantitative study employs a pre-experimental design with a pretest-posttest control group design. The instrument in this study used economic HOTS questions in the form of 15 multiple choice questions to determine the increase in ability to solve HOTS questions with the application of the Problem-Based Learning teaching model. It is known that the pre-test distribution data analysis obtained a mean of 58.64. In contrast, the post-test after being given a stimulus or treatment with the HOTS approach obtained a mean of 86.70, resulting in an increase in the results of the HOTS post-test after applying the Problem-Based Learning teaching model. There is a significant difference through the results of the students' pre-test and post-test, as evidenced by the t-test obtained t-count > t-table (6.855 > 1.675) and the Wilcoxon Sign rank test, which produces a value (asym.sig.(2-tailed)) is 0.000 <0.05. Then, the results of hypothesis H0 are rejected, and H1 is accepted. It can be concluded that there is an effect after applying the Problem-Based Learning teaching model in increasing the ability to solve HOTS questions