Statistical Analysis Of Data Research Articles

How to Perform a Statistical Analysis of Non-Destructive Degradation Data to Study Crack Growth in Wind Blades as a Function of the Number of Cycles

Objective: The aim of this paper is to show the application of the Exponential Regression Model to study the failure time of a system over time. The data was taken from the paper (Wang et al., 2021). Theoretical framework: The linear regression model, where the response is associated with the explanatory variables by means of a linear model, is the best known. To formulate the model, it is necessary to specify a deterministic component and a random (stochastic) component. The exponential model should be used when it is assumed that risk is constant over time. Once the model has been specified, its parameters are estimated. In the absence of normal errors and especially in the presence of censoring, a more appropriate option is the maximum likelihood method. As the equations are non-linear in their parameters and do not have an analytical solution, it is necessary to use the Newton-Raphson numerical method. Due to the simplicity of the exponential regression model, few situations in practice are adequately adjusted by this model. The Weibull regression model is widely used in survival analysis (Bastos Lyra et al., 2008) Method: Data taken from the article (Wang et al., 2021) e foi utilizado a Regressão Exponencial para modelar os dados. Final Considerations: This result indicates that Exponential Regression is a very viable option when the aim is to monitor the number of cracks as a function of time to check whether a system will fail. In this specific Case Study, it is clear that one of the blades is better than the others and that there are some Probability Distribution models that are suitable for the model. Implications of the research: The use cases of Exponential Regression are more restricted in the scientific literature and because of this, this case study is interesting to show that this model is effective for treating data in the area of reliability. Originality/value: Despite being a well-known statistical tool, Exponential Regression has a specific application in monitoring the number of cracks over time in a system.

Employment Discipline And the Threat of Dismissal Asregulators of the Labor Market in Kazakhstan (SDG)

Objective: This study investigates the role of unemployment as a mechanism of labor discipline in Kazakhstan. It analyzes the relationships between the official unemployment rate and the labor legislation reforms regulating labor discipline that have occurred since Kazakhstan gained independence. The manuscript outlines the developmental stages of labor legislation concerning labor discipline. Method: The research is based on a comprehensive analysis of statistical data, legislative acts, and judicial practices in Kazakhstan. Data sources include official statistics on unemployment and labor disputes, an analysis of changes in the Labor Code of Kazakhstan, and a review of judicial decisions related to disciplinary sanctions and dismissals. Results: The study reveals that strengthening disciplinary responsibility and expanding the powers of employers have significantly contributed to the decline in unemployment in Kazakhstan. These results support the hypothesis that strict labor discipline and pressure on employees can positively impact the overall labor market. However, this impact is not isolated but part of a broader set of measures. Contribution: This research provides a detailed comparative analysis of the number of labor disputes and the evolution of Kazakhstan's labor discipline legislation. Key observations include the correlation between stringent disciplinary norms and reduced unemployment, the influence of judicial practices on labor discipline, and the necessity of balancing strict discipline with fair treatment to prevent legal disputes. The findings offer valuable insights for optimizing labor policies to support sustainable labor market development in Kazakhstan. These insights align with the goals of SDG 8, which promotes sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all.

Analysing traffic safety on the roads of the Kharkiv region during the period of martial law

Problem. The functioning of road transport in regions of long-term armed conflicts is associated with additional risks for road traffic safety. The degree of influence of additional risk factors can be investigated by analyzing traffic accident statistics in such a region. Goal. The purpose of the work is to study the impact of martial law conditions on road safety indicators in the Kharkiv region of Ukraine by comparing statistical data on accidents and road deaths due to road accidents. Methodology. To achieve the goal, the work uses methods of statistical analysis of data and construction of trends, methods of determining relative indicators of the severity of road accidents. Results. As a result of the analysis of statistical data on road accidents in the Kharkiv region, the distribution of accident rates for the period from 2019 to 2023 was obtained, which made it possible to reveal their significant differences during the period of martial law compared to previous periods. In particular, it was established that with a decrease in the total number of road accidents, there was an increase in the severity of the consequences of road accidents. This regularity was confirmed by analyzing the relative indicators of road accident severity. The identified differences in accident and death rates on the roads of the Kharkiv region in 2023 compared to the periods before the start of hostilities indicate a change in road traffic conditions. Originality. The paper investigated the impact of martial law conditions on road safety on the example of the Kharkiv region of Ukraine. As a result, the degree of influence of the risk factors that arose as a result of the deployment of the armed conflict on the increase in the level of danger for road users was established. Practical value. The obtained results can be used for planning and implementation of measures to ensure traffic safety on highways in regions that are the zone of long-term armed conflicts.

Exploratory factor analysis for machining error data of compressor blades based on dimensionality-reduced statistical analysis method

Abstract Various machining errors inevitably occur on aero-engine compressor blades, including leading-edge contour error, trailing-edge contour error, camber contour error, and more. The current complexity surrounding the numerous ma-chining error types and their obscure interrelationships imposes immense effort for aerodynamic analysis and hin-ders overall error control. Thus, elucidating error correlations to achieve error dimensionality reduction is imperative. This study pioneers a dimensionality reduction approach via factor analysis to conduct a comprehensive statistical analysis of 13 types of blade machining errors. The proposed technique can categorize the 13 errors into three groups, each dominated by a distinct common factor. To validate the accuracy of the extracted factors, the factor scores of the three identified latent factors are computed. Cluster analysis is then performed on the factor scores, and the clustering results are visualized by t-Distributed Stochastic Neighbor Embedding (t-SNE). Furthermore, boot-strap resampling establishes the 95% confidence intervals for the factor scores. Capitalizing on the grouping struc-ture uncovered by factor analysis, multiple linear regression models are built for the errors within each group, and then, a preliminary conjecture is made about the potential key error types for each group of errors based on the re-gression coefficients. This hypothesis is then evidenced by the statistical analysis of cross-section profile error data of 28 blades. The present work can not only optimize machining processes but also relax tolerance requirements and diminish the effort of aerodynamic analysis.

Neurotechnologies and Artificial Intelligence in Public Administration: Application Practice and Possible Ways of Development

Over the past few years, the field of artificial intelligence and neurotechnology has moved beyond the scope of exclusive scientific discussion to the realm of public policy. The state is an important participant in technological progress, which allows us to consider in detail the connection between government officials and neurosciences, because, according to a large number of scientists, it is this segment of sciences that will allow humanity to transition to a new technological order. The purpose of this study is to consider the theoretical foundations of the interaction of the subjects of the public administration system with end-to-end technologies and to search for practical examples of the implementation of this interaction. In the course of the work such methods as theoretical analysis, comparison and contrast, cognitive method, system analysis, and analysis of statistical data were used. The theoretical foundations of the study of neurotechnologies, as well as the market of the existing neuroprosthetics products, were considered. The authors of the research studied and compared examples of the development of plans and the application of artificial intelligence and neurotechnologies in such countries as Russia, the United States, and the United Kingdom, and analysed global rankings of digitalisation of public administration. Based on this, it was concluded that countries are actively participating in a new technological race, trying to introduce artificial intelligence in the field of public administration in order to gain their own advantage, however the sluggishness of states in the development of neurotechnologies, with subsequent implementation in the public sector, was noted, and the fact of significant differences in the understanding of artificial intelligence in public administration around the world was revealed. This fact creates a field for further research and discussion. The results of the research can be used in the framework of further study of the analyzed aspects by scientific and research organizations, within the framework of the activities of federal authorities, as well as private companies.

Анализ динамики численности постоянного населения России: вызовы и перспективы

The article analyzes the dynamics of the permanent population of Russia in the period from 2018 to 2024 based on data from the Federal State Statistics Service. The main demographic challenges, such as population decline, increased emigration and population aging, as well as their impact on the economy and social sphere of the country are considered. The study pays attention to regional differences in the demographic situation, as well as factors influencing the population decline. Particular attention is paid to government support measures aimed at stabilizing the demographic situation, including measures to stimulate the birth rate, reduce emigration and support the labor market. Based on the analysis of statistical data, key trends in Russia's demographic policy are identified, recommendations are proposed to reduce the negative consequences of the demographic decline and improve the socio-economic situation in the country. The work emphasizes the importance of an integrated approach to solving demographic problems and the need to take long-term measures to ensure the sustainable development of the country.

A study on the professional attention and career commitment of health management students.

The education process is based on leading the choice of profession in line with the interests and abilities of the individual and helping them with their career plans in these professions. The aim of this study is to investigate the effect of the occupational interest levels of the Health Management Department students on their career commitment. In the study, descriptive findings were included by using a quantitative research design. In the research, data were collected from 282 students studying Department of Health Management. SPSS 26.0 package program was used for statistical analysis of research data. Parametric tests (t-test, analysis of variance, Pearson correlation analysis, normal linear regression analysis) were utilized since the data fit the normal distribution. 57.1% of the individuals participating in the research are female, 47.8% have the age range of 21 to 22. There is a positive, moderate (r = 0.677 p < 0.01) significant relationship between professional interest and career commitment scales. The level of professional interest and career dedication varies according to gender, age, income status and the class of the student. It is very important that health management students have an interest in their profession. Because this interest will guide them in what kind of a career they should make in their business life after graduation.

PENGARUH GAYA KEPEMIMPINAN GUBERNUR RIDWAN KAMIL TERHADAP KUALITAS PELAYANAN DI JAWA BARAT

This study aims to describe the significance of the influence of the leadership style of the West Java Governor for the 2018-2023 period on the quality of public services in the West Java Provincial Government in 2023. West Java Province is the most populous province in Indonesia, so it is a challenge for the West Java provincial government to provide excellent public services for all its citizens. The influence of a leader who can make efforts to improve the quality of public services is needed. The method used is quantitative. The data collection technique was carried out by distributing questionnaires to 100 respondents. In this research, a series of statistical data analysis was carried out to test the proposed hypothesis, the results of which were presented descriptively. The results obtained were 14.190&gt;1.661 with a significance value of &lt;0.001 and a correlation of 0.820 or in a very strong category. Where the leadership style of the West Java Governor for the 2018-2023 period has an effect of 66.9% on the quality of public services in the West Java Provincial Government in 2023.

Determinants of Peri-urban Land Use Management Practices in Case of Assosa City, Western Ethiopia

Peri-urban areas are today&amp;apos;s metaphors for several rapidly changing and competing socioeconomic aspects of urban and rural power. Suburban areas are facing the challenge of active, illegal, and unauthorized spatial transformations. The division between the institutional and legal frameworks also exacerbates the issue. Robust and holistic land management are prospects for today&amp;apos;s peri-urban areas to become tomorrow&amp;apos;s huge cities. This study aims to examine determining factors of peri-urban land management practice in Assosa city peri-urban areas to support the design of effective land management systems. A mixed method study was used to collect primary and secondary data sources. The sample of peri-urban households was selected using simple random sampling techniques. Both descriptive and inferential statistical data analysis methods were utilized. The findings demonstrated that the bifurcated and ineffective legal and institutional frameworks, limited public-private organizational support and participation influence awareness and motivations of peri-urban landholders land management practice in suburban settings. Following this, the majority of peri-urban landholders consequently lack access to the land management practice. The estimated results of the binary logit model show the following variables: awareness, motivation, participation, institutional setup and land disputes were found to be factors determining peri-urban land management practice. The study recommends an all-in-one robust institutional framework to promote sustainable land management.

Ten questions concerning statistical data analysis in human-centric buildings research: A focus on thermal comfort investigations

Given the large amount of time we spend indoors, designing and operating buildings that are safe, comfortable, and conducive to productivity and well-being is essential. To achieve this goal, in the past decades, research has been conducted to investigate the influence of the indoor environment on occupants. Thermal comfort has been the subject of most investigations in this field. However, despite being a consolidated research topic since the 1920s, statistical practices for analysing thermal comfort data often rely on simplified premises, which may be due to several possible factors (e.g., limited computational capabilities and lack of training). Consequently, important aspects of data analysis are often absent or overlooked. Recent statistics and statistical software advances have provided more options for effectively modelling complex issues. However, properly using these tools requires a solid understanding of statistical analysis, increasing the risk of misuse in practice. This paper presents ten questions highlighting the most critical issues regarding statistical analysis for thermal comfort research and practice. The first four questions provide general perspectives concerning statistical data analysis, while the remaining ones address specific problems related to thermal comfort research, but that can extend to all human-centric research in the built environment. Additionally, the last five questions demonstrate the practical significance of analysis pitfalls (i.e., sampling variability, selection bias, variable selection, clustered/nested observations, and measurement error) through examples with synthetic data. This study provides insights into the current statistical ‘habits’ in thermal comfort research and, more importantly, help researchers better define and conduct their statistical analyses.

Эффективность производства зерна в условиях изменения климата в Зауралье

Abstract. The economic efficiency of grain production is determined by the level of return on the resources spent by the cost of the products received. Weather factors play an important role. Climate changes in the Trans-Urals are primarily associated with the warming of the spring and summer months, increased aridity of the growing season and the expansion of the boundary of this process from the southeast to the northwest. The contrast in moisture availability of different years is combined with uneven precipitation in the region throughout the year. The profitability of farms in the production of grain crops is characterized by the same heterogeneity. The purpose of the study is to establish the economic efficiency of grain production in the context of climate change using the example of the Trans-Urals. The tasks are to analyze the impact of climate change on the yield of grain crops in the region, to analyze the efficiency of resource use by agricultural enterprises; using the example of the most highly profitable farms to identify factors to increase the efficiency of grain production. The methods of monographic and statistical analysis of data from scientific literature, open statistical sources, agronomic and economic reports of agricultural enterprises were used. The scientific novelty of the research consists in studying the efficiency of grain production in the context of climate change and in relation to the volatility of grain prices and means of production. The results of the study showed that since the early 2000s, despite a significant increase in the use of fertilizers, the growth of grain yields has stopped, which is primarily due to the deterioration of plant moisture conditions. At the same time, an increase in costs, although it leads to an increase in productivity, but to a lesser extent, as a result, the cost of grain increases, and the profitability of production decreases. Enterprises have been identified that, in the conditions of climate change and high volatility of grain prices and resources, have found ways to efficiently produce grain: replace solid fertilizers with liquid fertilizers, apply effective, including agrotechnical, methods of tillage and plant protection, use highly productive varieties, obtaining the highest profitability in the region.

Development of national economic systems in the context of transformation of globalization and regionalization processes

Relevance. In the context of the formation of a multipolar world and the increasing sanctions pressure from unfriendly countries, Russia needs to adjust the models of integration into the system of world economic relations. In this context, an in-depth analysis of the mechanisms of internationalization, globalization and regionalization is required.The purpose is to consider the peculiarities of the impact of globalization and regionalization processes on national economies, taking into account the transformation of their internationalization models.Objectives: to consider the concepts of internationalization, regionalization and globalization, to identify their relationship; to identify the advantages and disadvantages of globalization, on the basis of which to describe the directions of transformation of forms of internationalization of economies; based on the analysis of available statistical data, to identify promising areas of integration of the national economy into the system of world economic relations.Methodology. The methods of quantitative and qualitative analysis, monographic and retrospective analysis, methods of comparing economic phenomena and processes, methods of abstraction, generalization of analyzed economic information are used.Results. The internationalization of national economies is a long-term sustainable trend. Their development is confirmed by sufficiently convincing quantitative data. Recently, the dynamics of the corresponding time series has become unstable, volatile and turbulent, with a tendency to decrease the level of indicators. There are transformations of the processes of globalization and regionalization, they acquire not only a new quality, but also institutional consolidation.Conclusions. The traditional model of globalization has exhausted itself. Due to the formation of a multipolar political and economic global space, in addition to the "center" and "periphery", countries of the "semi-periphery" stand out in it. They have sufficient economic sovereignty to refuse to participate in globalization projects, the balance of effects of which for these countries becomes negative. Russia is one of these countries. An additional incentive for reconfiguring the Russian model of internationalization of the economy is the anti-Russian sanctions imposed by the countries of the "center". In this regard, it is necessary to open up new opportunities for the national economy to integrate into the global economic space based on models of regionalization and glocalization.

Pengaruh Jam Kerja dan Kompensasi terhadap Kepuasan Karyawan pada UP Angkutan Sekolah Dishub Provinsi DKI Jakarta

Human resources play an important role in the future development of the organization. A person works to give his time and energy to the organization as a counter achievement and the organization provides rewards or compensation whose forms can vary greatly. The main key that must be considered by companies in retaining their employees is to prioritize their job satisfaction. Therefore, things that affect the increase in job satisfaction, such as working hours and compensation must be considered. This study aims to analyze the effect of working hours and compensation on employee satisfaction at UP Angkutan Sekolah Dishub DKI Jakarta. The approach used in this research is a quantitative approach with statistical data analysis techniques. The data was collected through distributing questionnaires to 84 employees, and processed using SPSS software version 25. The analysis techniques used include data quality test, classical assumption test, hypothesis testing and coefficient of determination test. The results of this study indicate that partially working hours have no influence and are significant to employee satisfaction, while compensation partially shows that there is an influence and is significant to employee satisfaction. Simultaneously working hours and compensation simultaneously have a positive and significant effect on employee satisfaction.

The Association of Vitamin D Receptor Gene Polymorphism with Polycystic Ovary Syndrome in a Southeast Iranian Population

Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder among women of reproductive age, characterized by hyperandrogenism, amenorrhea, and polycystic ovaries. Metabolic disorders, including insulin resistance (IR), are common comorbidities of PCOS, with vitamin D receptors playing a crucial role. Objectives: We investigated whether the rs2228570T &gt; C variant of the vitamin D receptor (VDR) gene is associated with the risk of PCOS. Methods: In the current study, 112 women with PCOS and 150 healthy women participated. Genomic DNA was extracted using the standard salting-out method. Genotyping of rs2228570T &gt; C was conducted via amplification-refractory mutation system polymerase chain reaction. Additionally, VD3 levels were measured using the ELISA system. Statistical analysis of genotyping data was performed using the SPSS V.22 package. Results: The statistical analysis showed that the T-allele of rs2228570C/T significantly decreased the risk of PCOS. Moreover, the T-allele in the codominant model significantly eliminated the risk of PCOS in our population. Our results also showed a statistically significant difference in VD3 levels between the CC and TT genotypes. Conclusions: Based on our result, rs228570C/T had a protective role for PCOS risk in our population.

Co-observation of germline pathogenic variants in breast cancer predisposition genes: Results from analysis of the BRIDGES sequencing dataset

Co-observation of a gene variant with a pathogenic variant in another gene that explains the disease presentation has been designated as evidence against pathogenicity for commonly used variant classification guidelines. Multiple variant curation expert panels have specified, from consensus opinion, that this evidence type is not applicable for the classification of breast cancer predisposition gene variants. Statistical analysis of sequence data for 55,815 individuals diagnosed with breast cancer from the BRIDGES sequencing project was undertaken to formally assess the utility of co-observation data for germline variant classification. Our analysis included expected loss-of-function variants in 11 breast cancer predisposition genes and pathogenic missense variants in BRCA1, BRCA2, and TP53. We assessed whether co-observation of pathogenic variants in two different genes occurred more or less often than expected under the assumption of independence. Co-observation of pathogenic variants in each of BRCA1, BRCA2, and PALB2 with the remaining genes was less frequent than expected. This evidence for depletion remained after adjustment for age at diagnosis, study design (familial versus population-based), and country. Co-observation of a variant of uncertain significance in BRCA1, BRCA2, or PALB2 with a pathogenic variant in another breast cancer gene equated to supporting evidence against pathogenicity following criterion strength assignment based on the likelihood ratio and showed utility in reclassification of missense BRCA1 and BRCA2 variants identified in BRIDGES. Our approach has applicability for assessing the value of co-observation as a predictor of variant pathogenicity in other clinical contexts, including for gene-specific guidelines developed by ClinGen Variant Curation Expert Panels.

The Guidance of Public Value in China’s Historic Environment: Research on Regeneration Strategies Using Taiyuan’s Bell Tower Street as an Example

When profit-driven renewal is difficult to implement, many historic districts in China become “frozen” under protection. In the recent social context of “building cities for the people”, public value-oriented renewal of urban historic areas has become crucial in city transformation. This study investigates strategies for historic district regeneration in China by integrating spatial form, urban governance, and public value. We propose a novel framework to analyze the regeneration of historic districts, using Taiyuan’s Bell Tower Street as a case study. The framework distinguishes between two dimensions: spatial and economic–social. In the spatial dimension, we focus on the transformation of spatial form to regenerate public value, emphasizing the attribute of “publicness” as central to urban regeneration efforts. This involves a tiered approach to excavating and upgrading historic districts at macro, meso, and micro levels. The economic–social dimension explores urban governance to enhance public value through collaboration between government, market, and societal actors. This study employs a mixed-methods approach, including document reviews, interviews, field observations, and statistical data analysis. The analysis highlights that a public value-oriented approach to heritage regeneration can balance historical preservation with contemporary urban needs, offering a sustainable model for other cities.

Measurement of medical haemodilatation based on statistical analysis of big data

Measurement of medical haemodilatation based on statistical analysis of big data

Development of a Quality Deterioration Index for Sustainable Quality Management in High-Tech Electronics Manufacturing

In high-tech electronics manufacturing, non-quality costs significantly impact organizational profitability and competitiveness. This case study introduces a novel Quality Deterioration Index (QDI) to systematically identify and prioritize root causes of non-quality costs within a leading electronics manufacturer. The primary objective is to integrate sustainable quality management practices that align with green sustainability objectives, such as reducing electronic waste, improving energy efficiency, and minimizing hazardous materials usage. Our comprehensive methodology encompasses a literature review, interviews, document analysis, and statistical analysis of survey data to uncover the influence of procedural, cultural, and environmental factors on quality deviations. The key findings reveal critical areas for improvement, particularly in supply chain inefficiencies, workforce challenges, and procedural gaps. By employing the QDI, we provide a structured framework that enhances both operational efficiency and environmental performance. The novelty of this research lies in its dual approach to simultaneously address economic and environmental performance, offering actionable insights for manufacturers aiming to integrate robust quality management systems with sustainability objectives. This study contributes to the ongoing dialogue on sustainable manufacturing strategies, underscoring the pivotal role of quality management in achieving both economic viability and environmental stewardship. Future research should expand this approach across various industries and global contexts to validate and refine the integration of quality management and sustainability.

Differences in coaching in single- versus dual-console robotic cases: a mixed-methods study.

A growing importance has been placed on development of trainee robotic surgical skills through simulation curricula and intraoperative experience. However, few studies have examined how console case type impacts learning outcomes.We sought to evaluate how intraoperative coaching and resident autonomy differ based on the use of a single- versus dual-console robot. Robotic single- and dual-console cases from February to September 2023 at a single institution were included. Faculty and trainees wore microphones to capture audio during the case. Pre/post surveys were administered, which included metrics on faculty coaching based on the Wisconsin Surgical Coaching Rubric (WiSCoR) and on trainee technical performance based on the Global Evaluative Assessment of Robotic Skills (GEARS). Statistical analysis of survey data was performed using SPSS. Audio from cases was coded by 2 researchers with a deductive approach using WiSCoR as a framework. Data were collected for 7 (38.9%) single and 11 (61.1%) dual-console cases across 9 case types from 4 surgical specialties. Chi-square analysis demonstrated no significant difference in percentage of case trainee spent in the operating surgeon role based on trainee level or console case type. Independent t-tests showed no significant difference in trainee autonomy, trainee performance, or faculty coaching scores based on console case type. Trainees rated faculty highest in WiSCoR Domains 1 (sharing responsibility) and 3 (providing constructive feedback). Qualitative analysis showed that for single-console cases, Domain 4 (goal setting) was most represented (34.0% of comments), while for dual-console cases, Domain 1 was most represented (37.0% of comments). Qualitative analysis highlights that despite similar survey-based faculty ratings across domains, coaching on self-reflection (Domain 2) is infrequently done, highlighting an opportunity for improvement in this area of coaching during robotic surgery.

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference.

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. We present a spectral decomposition method that allows for corrections to line positions and bound-bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.