Modeling Of Factors Research Articles

A multilevel Bayesian Markov Chain Monte Carlo Poisson modelling of factors associated with components of antenatal care offered to pregnant women in Nigeria

BackgroundThe most recent WHO guideline on antenatal care (ANC) utilization reaffirmed the necessary and compulsory care and services a pregnant woman should receive to maximize the importance and gains of ANC. While most studies focused on the time of initiation and number of ANC contacts, emphasis was rarely placed on the components of ANC offered to women. This study assessed how complete the components of ANC received by pregnant women are as a proxy for the quality of ANC services offered in Nigeria. We also assessed the clustering of the components and state-level differentials and inequalities in the components of ANC received in Nigeria.MethodsWe used nationally representative cross-sectional data from the 2018 Nigeria Demographic Health Survey. We analysed the data of 11,867 women who had at least one ANC contact during the most recent pregnancy within five years preceding the survey. The assessed components were tetanus injection, blood pressure, urine test, blood test, iron supplement, malaria intermittent preventive treatment in pregnancy (IPTp), and told about danger signs. Others are intestinal parasite drugs (IPD)intermittent and HIV/PMTCT counsel. Descriptive statistics, bivariable and multivariable multilevel Bayesian Monte Carlo Poisson models were used.ResultsIn all, 94% had blood pressure measured, 91% received tetanus injection, had iron supplement-89%, blood test-87%, urine test-86%, IPTp-24%, danger signs-80%, HIV/PMTC-82% and IPD-22%. The overall prevalence of receiving all 9 components was 5% and highest in Ogun (24%) and lowest in Kebbi state (0.1%). The earlier the initiation of ANC, the higher the number of contacts, and the higher the quality of ANC received. Respondents with higher education have a 4% (adjusted incidence risk ratio (aIRR): 1.04, 95% credible interval (CrI): 1.01–1.09) higher risk of receiving more components of ANC relative to those with no education. The risk of receiving more ANC components was 5% (aIRRR: 1.05, 95% CI: 1.01–1.10) higher among pregnant women aged 40 to 49 years than those aged 15 to 19 years. Women who decide their healthcare utilization alone had a 2% higher risk of getting more components than those whose spouses are the only decision taker of healthcare use. Other significant factors were household wealth status, spouse education, ethnicity, place of ANC, and skill of ANC provider. Pregnant women who had their blood pressure measured were very likely to have blood and urine tests, tetanus injections, iron supplements, and HIV talks.ConclusionsOnly one in every 20 pregnant women received all the 9 ANC components with wide disparities and inequalities across the background characteristics and the States of residence in Nigeria. There is a need to ensure that all pregnant women receive adequate components. Stakeholders should increase supplies, train, and create awareness among ANC providers and pregnant women in particular.

Вплив об’єктивних і суб’єктивних факторів на народжуваність у періоди невизначеності

The article examines a complex of objective and subjective factors that determine fertility on a macro and micro level, as well as the specificity of their relationships during periods of uncertainty. The purpose of this study is to identify objective and subjective factors of fertility, analyze the relationships between them, and the characteristics of their influence under conditions of socio-economic and military-political uncertainty in Ukraine. To accomplish this, a range of methods were employed, including systematization and generalization, critical evaluation of scientific progress in the relevant field, categorization, conceptual modeling of factors affecting demographic processes (reproductive behavior and fertility), structural-logical analysis, comparative analysis and others. The novelty of the work lies in identifying the nature of the influence of objective and subjective factors, their interrelationships and role in the determination of fertility on a macro and micro level in conditions of uncertainty. Objective and subjective factors are interrelated and, depending on the context and level of fertility determination (macro or micro level), their significance and the nature of influence differ. The specificity of the relationship between objective and subjective factors of fertility is reflected in the “transition” of ones into others at various levels. The influence of objective factors on fertility is mostly mediated and occurs through subjective perception by individuals of any particular objective factors and circumstances. Subjective perceptions at the micro level are characterized by variability and may not coincide with the nature of changes in objective factors. In conditions of uncertainty and social upheavals (such as economic crisis, pandemic, war, etc.), discrepancies increase, and the influence of the subjective in fertility determination increases and takes on special characteristics. One manifestation of this is the increased role of expectations for the future (narratives of the future) in reproductive decision-making. Positive narratives about the future, reinforced by the implementation of measures to improve the quality of life for the population in a free democratic space, could become a driving force for increasing birth rate in Ukraine, parti - cularly for strengthening the compensatory effect in its dynamics after the war.

Modeling of Risk Factors of Childhood Stunting Cases in Malang Regency using Geographically Weighted Regression (GWR)

Background: Stunting is one of the fundamental problems in Indonesia's human development, with complex risk factors such as inadequate environmental sanitation. Objective: This study aims to conduct an analysis and mapping of spatially correlated sanitation risks on the prevalence of stunting among toddlers in Malang Regency, Indonesia. Methods: The field observations were conducted in 390 villages of urban and rural areas in Malang Regency. The secondary data of weighing toddlers in 2020 were collected from Malang Regency Health Office. The data included the identity of the residents and the history of the toddlers, while data from monthly program reports were collected from the Central Bureau of Statistics of Malang Regency. Furthermore, the study applied one and eight variables of Y and X. The Y variable was the number of stunting case toddlers, while X included the percentage of access to safe drinking water, percentage of access to permanent hygienic latrines, the topography of the region, percentage of Integrated Healthy Centre access, percentage of Exclusive Breastfeeding, Population density, ODF (Open Defecation Free) village and percentage of Integrated Healthy Centre Empowerment. Modeling the case of childhood stunting with the spatial regression method using Geographically Weighted Regression can be considered a sound approach. Results: The result showed that the distribution model of risk factors causing toddler stunting showed a spatial autocorrelation tendency with a clustered pattern in the area. The risk factors that were found to be spatially correlated with the incidence of stunting in children under five years of age in Malang district included access to proper drinking water (X1), access to latrines (X2), access to integrated health services (X3), exclusive breastfeeding coverage (X4), regional topography (X5), population density (X6), and empowerment of integrated health services (X8). However, the ODF (X7) did not show any correlation with stunting cases in the area. Conclusion: Spatial diversity and geographic variation affect the distribution pattern of risk factors causing stunting in Malang Regency. Therefore, stunting control with specific and sensitive interventions should be based on regional and local characteristics.

Causal Modelling of Factors Influencing Quality of Nursing Care in China

The quality of nursing care is essential and contributes to restoring people’s health and well-being, especially when people are hospitalized. Various factors relating to the quality of nursing care have been identified, including nursing shortages. However, in China, there has been little research undertaken on factors influencing nursing care quality. This cross-sectional study aimed to develop and test the Chinese Model of Quality Nursing Care. A multi-stage, proportional stratified random sampling was used to recruit 784 registered nurses in three affiliated hospitals of a medical university in the People’s Republic of China. Eight Instruments were used for data collection: a demographic data form, the Good Nursing Care Scale, the Nurse Staffing Item, the Practice Environment Scale of the Nursing Work Index, the 3-item Utrecht Work Engagement Scale, the Psychological Empowerment Scale, the High-performance Work Systems Scale, the 8-item Survey of Perceived Organizational Support. In addition, the Analysis of Moment Structure software program was used to test the hypothesized model. Results indicated that the modified model fitted the empirical data and explained 31% of the variance in the quality of nursing care. Nursing practice environment, psychological empowerment and work engagement had positive direct effects on the quality of nursing care, with the nursing practice environment having the strongest total effect. Nursing practice environment, high-performance work systems, and perceived organizational support indirectly positively affected the quality of nursing care through psychological empowerment and work engagement. Unexpectedly, the patient-to-nurse ratio had no significant influence on the quality of nursing care. The results support the need for hospital managers to take action to improve the organizational support and work environment and create high-performance work systems to retain more nurses and midwives to enhance the quality of nursing care. The model requires further testing in practice and possible refinement.

Fuzzy-AHP Modelling of Risk Factors for Tanneries in Bangladesh to Adapt Industry 4.0

Fuzzy-AHP Modelling of Risk Factors for Tanneries in Bangladesh to Adapt Industry 4.0

New benchmarks in the modelling of X-ray atomic form factors.

Analytical representations of X-ray atomic form factor data have been determined. The original data, f0(s;Z), are reproduced to a high degree of accuracy. The mean absolute errors calculated for all s = sin θ/λ and Z values in question are primarily determined by the precision of the published data. The inverse Mott-Bethe formula is the underlying basis with the electron scattering factor expressed by an expansion in Gaussian basis functions. The number of Gaussians depends upon the element and the data and is in the range 6-20. The refinement procedure, conducted to obtain the parameters of the models, is carried out for seven different form factor tables published in the span Cromer & Mann [(1968), Acta Cryst. A24, 321-324] to Olukayode et al. [(2023), Acta Cryst. A79, 59-79]. The s ranges are finite, the most common span being [0.0, 6.0] Å-1. Only one function for each element is needed to model the full range. This presentation to a large extent makes use of a detailed graphical account of the results.

Interpretive structural modelling of organizational innovation factors: An emerging market perspective

This research aims to identify key factors in organizational innovation and their interrelationships using Interpretive Structural Modelling (ISM). An interpretive review of related literature was utilized to develop a multi-level structured model ISM with the help of MICMAC analysis to determine the driving and dependence power of each identified factor. 11 factors were identified and the extent to which they influence organizational innovation was explored. Diversity, empowerment, and Training & Development are the strongest driving forces (independent factors), whereas innovation and organizational culture had the lowest driving power and highest dependence (dependent factors). This study provides a foundation for further research development and managerial practice to emphasize major factors driving organizational innovation. However, the application of ISM model incorporates subjective judgments that may affect the results. This research extends beyond existing literature in integrating a plethora of factors influencing and inhibiting innovation in organizations, and in providing an explicit and comprehensive view of these factors’ interrelationships and mutual primary effects, with implications for promoting employee creativity and thus organizational innovation.

Causal Modelling of the Key Competitiveness Assessment Factors of Wellness Tourism Destinations: A DEMATEL Approach

Objectives: The objectives of this study are 1) to identify and prioritize the significant competitiveness factors that need to be considered in the context of wellness tourism destinations and 2) to identify the causal interrelationships between competitiveness factors that need to be considered in the context of wellness tourism destinations. Methods/Analysis: The theoretical framework of this research began with documentary research of secondary data, followed by semi-structured interviews and quantitative research using questionnaires to collect primary data from wellness tourism clusters and experts. Based on these responses, a multiple-criteria decision-making framework using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was implemented to prioritize wellness tourism destination competitiveness factors and their relationships. Findings: The destination strategy and roadmap for wellness tourism were the most important competitiveness factors for wellness tourism destinations, and the wellness service experience and activities factor was the most significant cause factor, with strategies to improve service and restoration being the most significant source of effect. Novelty/Improvement:This research sheds light on wellness tourism destinations' competitiveness assessment factors and indicators and how to improve them to attract wellness tourists seeking preventive health care or health promotion services. Doi: 10.28991/HEF-2023-04-02-01 Full Text: PDF

Local and regional factors of spatial differentiation of the excess mortality related to the COVID-19 pandemic in Romania.

COVID-19 revealed some major weaknesses and threats that are related to the level of territorial development. In Romania, the manifestation and the impact of the pandemic were not homogenous, which was influenced, to a large extent, by a diversity of sociodemographic, economic, and environmental/geographic factors. The paper is an exploratory analysis focused on selecting and integrating multiple indicators that could explain the spatial differentiation of COVID-19-related excess mortality (EXCMORT) in 2020 and 2021. These indicators include, among others, health infrastructure, population density and mobility, health services, education, the ageing population and distance to the closest urban center. We analyzed the data from local (LAU2) and county level (NUTS3) by applying multiple linear regression and geographically weighted regression models. The results show that mobility and lower social distancing were far more critical factors for higher mortality than the intrinsic vulnerability of the population, at least in the first two years of COVID-19. However, the highly differentiated patterns and specificities of different areas of Romania resulting from the modelling of EXCMORT factors drive to the conclusion that the decision-making approaches should be place-specific in order to have more efficiency in case of pandemics.

Methane emissions from rice paddies globally: A quantitative statistical review of controlling variables and modelling of emission factors

Greenhouse gas (GHG) modelling tools or the Intergovernmental Panel on Climate Change (IPCC) inventory methods are often used to identify suitable mitigation strategies for GHG emissions from rice, since measuring them in field is challenging and costly. Here we report an up-to-date quantitative review on methane (CH4) emission from rice paddies using information obtained from peer-review articles. Statistical analysis was conducted on the factors controlling CH4 emissions and a generalised additive model (GAM) was developed to estimate emission factors (EFs). Results showed that emissions were strongly linked to water regime, soil texture and organic amendment practices. Fields that were rainfed during the dry season or saturated emitted 70% and 56% that of continuously flooded fields, while applying straw off-season instead of within-season could decrease emissions by 48%. An independent dataset was used to evaluate the new model performance against existing models with the new model showing R2 values of 0.47 (n: 169), compared to 0.01–0.09 (n: 169) for the existing models. New baseline EFs was estimated at global, regional, and Country scale with result showing that using different pre-season water management when calculating baseline EFs at country level is vital in order to reflect the variation between tropical and temperate rice regions accurately. Our findings shows that the new model is more sensitive in capturing differences in management practices between tropical and temperate rice, and their impacts on CH4 emissions with baseline EF calculations accounting for these differences providing sound mitigation strategies.

Development of a nomogram model to predict malignant vasovagal syncope in Chinese children.

Vasovagal syncope (VVS) is the commonest form of syncope, and malignant VVS draws substantial attention due to its life-threatening cardiac asystolic risk. This study aimed to explore the predictive role of a wide panel of clinical indicators for malignant VVS in children, and further to develop a nomogram model. This is a retrospective case-control study. VVS is diagnosed based on head-up tilt test (HUTT). STATA software (version 14.0) was used for statistical analysis, and effect sizes are expressed as odds ratio (OR) and 95% confidence interval (CI). Total 370 children with VVS were analyzed, and of them 16 children had malignant VVS. Sixteen malignant VVS and 64 non-malignant VVS were matched on age and sex by a 1:4 propensity scores matching method. Mean corpuscular hemoglobin (MCH) and standard deviation of average RR intervals milliseconds (SDANN) were significantly and independently associated with malignant VVS after adjusting for confounders, with OR reaching 1.437 (95% CI: 1.044 to 1.979; P = 0.026) and 1.035 (95% CI: 1.003 to 1.068; P = 0.029), respectively. Calibration and discrimination analyses revealed that the addition of MCH and SDANN can enhance model performance. Then, a nomogram to predict malignant VVS was developed using general characteristics and two above significant factors, and higher values in medical history, number of syncope, MCH and SDANN were associated with a greater risk of malignant VVS. MCH and SDANN were two promising factors for the development of malignant VVS, and modeling of significant factors in a nomogram can provide strong reference to aid clinical decision-making.

Statistical analysis and modeling of factors of resource potential of agricultural development in the region

In modern realities, the issue of effective use of the existing resource potential of the regions is acute, the solution of which directly affects the effectiveness of the functioning of industrial complexes of both individual territories and the country as a whole. Agriculture consists of a system of interconnected industries that produce various types of agricultural products, ensuring the food security of the state and the quality of life of the population. The purpose of the work was to analyze the resource potential of regional agriculture and identify a set of relevant factors (based on processing the flow of statistical data) on the example of the Orenburg region. The economic and statistical apparatus using the Statistica software package was used as a tool for carrying out correlation and regression analysis and building models. The article analyzes various interpretations of the concept of "potential". The components of the resource potential are considered and the factors characterizing them are ordered. A comparison of a number of indicators of agricultural production (the volume of agricultural production in millions of rubles, the size of the acreage of crops, the volume of gross harvest of grain and potatoes, the number of cattle and pigs) by regions of the Volga Federal District is given. The leading subjects of the district have been identified, among them the Orenburg Region, on the materials of which methods of correlation and regression analysis have been implemented and a set of models has been built. As an example, an econometric model of the impact on the volume of agricultural products of a number of factors (characterizing the resource potential of the region) is given, including the area of agricultural land, the average annual number of agricultural workers, the introduction of organic and mineral fertilizers per hectare of crops, the cost of fixed assets by type of economic activity, the volume of investments in fixed capital of agriculture farms. The analysis of the variables included in the model is carried out. Taking into account the identified factors, a set of measures aimed at improving the efficiency of agricultural production in the region is formulated.

White analytical chemistry-driven stability-indicating concomitant chromatographic estimation of thiocolchicoside and aceclofenac using response surface analysis and red green blue model.

White analytical chemistry is a novel concept for the assessment of analytical methods on basis of its validation efficiency, greenness power and economical efficiency. White analytical chemistry-driven stability indicating chromatographic method has been developed for the concomitant analysis of thiocolchicoside and aceclofenac. The proposed chromatographic method has been developed using a safe and environmental-friendly organic solvent for the concomitant stability study of thiocolchicoside and aceclofenac. The analytical risk assessment was carried out for the identification of high-risk analytical risk factors and analytical method performance attributes. The mixture design was applied for the design of experiments-based response surface modelling of high-risk analytical risk factors and analytical method performance attributes. The degradation products were isolated and characterized using infra-red, nuclear magnetic resonance and mass spectral data. The proposed method was compared for its validation efficiency, greenness power and cost-efficiency with published chromatographic methods using the red, green and blue models. The white score of the proposed and reported method was calculated by averaging the red, blue and green scores of the methods. The proposed method was found to be robust, green and economical for the concomitant stability study of thiocolchicoside and aceclofenac. This article is protected by copyright. All rights reserved.

Analysis and modeling of innovation factors to replace fossil fuels with renewable energy sources - Evidence from European Union enterprises

In previous years, much research has been carried out to specify eco-innovations determinants. In most studies, eco-innovation was analyzed at a general level. From the perspective of climate change and the aim of achieving the Sustainable Development Goals and the European Green Deal objectives, the issue of innovations that replace the share of fossil fuel energy with renewable energy sources in the European Union appears to be particularly important. Therefore, this paper has two purposes. First, using a systematic literature review, it examines the key factors driving decisions taken by European Union enterprises to create innovations with environmental benefits. Second, it estimates the impact of these factors on introducing innovations that replace the share of fossil fuel energy with renewable energy sources. Specifically, using the logistic regression model, it analyses microdata from thirteen European Union Member States collected during the 2014 Community Innovation Survey. The effects show that six out of seven drivers under assessment positively affect the introduction of innovations that replace fossil fuel energy with renewable energy sources. These are (1) the high cost of energy, water, or materials, (2) environmental regulations or taxes expected in the future, (3) existing environmental taxes, charges, or fees, (4) government grants, subsidies, or other financial incentives for environmental innovations, (5) current or expected market demand for environmental innovations, and (6) voluntary actions or initiatives for good environmental practice within a sector.

Modeling of Factors that Influence on Early Neonatal Death due to Asphysia in South Central Timor District

The infant mortality rate is also an important indicator to reflect the state of health status in a society, progress in the field of prevention and eradication of various diseases that cause death will be clearly reflected by the decline in the level of IMR. Thus the infant mortality rate is a sensitive measure of all intervention efforts made by the government, especially in the health sector. The purpose of this study was to find out and analyze what factors had the most influence on early neonatal deaths due to asphyxia in Timor Tengah Selatan Regency in 2021. The design of this study used a case-control research design with a retrospective study approach. The population in this study was divided into 2, namely the case population were all live births that experienced asphyxia and died in early Neonatal and the control population in this study were all live births who had asphyxia and did not die in the 2020 period in the work area of the Health Service. South Central Timor Regency (TTS). The sample size in this study was taken using a total sampling technique, where the entire population was used as the research sample, namely as many as 26 case samples, with a sample size comparison between cases and controls was 1:1, so the total sample size was 52 samples. The results showed that there was an effect of gestational interval (95% CI = 3.20-47.834 p-value = 0.000), history of premature (95% CI = 1.836-20.315 p-value = 0.003) and asphyxia (95% CI = 4.509- 74.539 p-value = 0.000) on Early Neonatal Death in Timor Tengah Selatan Regency (TTS). There was no effect of parity on gestational age (p-value=0.213), anemia (p-value=0.432), Long Parturition (p-value=0.201) and ANC visits (p-value=0.211 on Early Neonatal Mortality in Timor Tengah Selatan Regency (TTS) The most dominant variable and the most influential on the incidence of Early Neonatal mortality is the Asphyxia variable.

Analysis of the milk market in Russia

The article is devoted to the issues of analysis, evaluation and modeling of factors influencing the formation and development of indicators characterizing the state of the milk market in Russia. The results of a study of the volumes of production and consumption of milk, both in the country as a whole and per capita, are presented. Proposed methodological approaches and models for assessing the impact of factors on the volume of production and consumption of milk per capita. The results obtained can be used to study macroeconomic processes in order to develop and adjust the socio-economic policy, the strategy of economic and food security of economic entities of various levels, as well as the rationale for management decisions.

Modeling of factors affecting late gadolinium enhancement kinetics in MRI of cardiac amyloid

BackgroundLate gadolinium enhancement (LGE) is a valuable part of cardiac magnetic resonance imaging (CMR). In particular, inversion-recovery imaging of LGE, with nulling of the signal from reference areas of myocardium, can have a distinctive pattern in some patients with cardiac amyloid, including both diffuse (relatively faint) subendocardial LGE and a relatively dark appearance of the blood. However, the underlying reasons for this distinctive appearance have not previously been well investigated. Pharmacokinetic modeling of myocardial contrast enhancement kinetics can potentially provide insight into the mechanisms of the distinctive LGE appearance that can be seen in cardiac amyloid, as well as why it may be unreliable in some patients. MethodsAn interactive three-compartment pharmacokinetic model of the dynamics of myocardial contrast enhancement in CMR was implemented, and used to simulate LGE dynamics in normal, scar, and cardiac amyloid myocardium; the results were compared with previously published values. ResultsThe three-compartment model is able to capture the qualitative features of LGE, in patients with cardiac amyloid. In particular, the characteristic “dark blood” appearance of PSIR images of LGE in cardiac amyloid is seen to likely primarily reflect expansion of the extravascular extracellular space (EES) by amyloid in the “reference” myocardium; the cardiac amyloid contrast enhancement dynamics also reflect expansion of the body EES. ConclusionThe distinctive appearance of LGE in cardiac amyloid is likely due to a combination of diffuse expansion by amyloid of the EES of the reference myocardium and of the body EES.

Geospatial Modeling of Health, Socioeconomic, Demographic, and Environmental Factors with COVID-19 Incidence Rate in Arkansas, US

The COVID-19 pandemic has posed numerous challenges to human society. Previous studies explored multiple factors in virus transmission. Yet, their impacts on COVID-19 are not universal and vary across geographical regions. In this study, we thoroughly quantified the spatiotemporal associations of 49 health, socioeconomic, demographic, and environmental factors with COVID-19 at the county level in Arkansas, US. To identify the associations, we applied the ordinary least squares (OLS) linear regression, spatial lag model (SLM), spatial error model (SEM), and multiscale geographically weighted regression (MGWR) model. To reveal how such associations change across different COVID-19 times, we conducted the analyses for each season (i.e., spring, summer, fall, and winter) from 2020 to 2021. We demonstrate that there are different driving factors along with different COVID-19 variants, and their magnitudes change spatiotemporally. However, our results identify that adult obesity has a positive association with the COVID-19 incidence rate over entire Arkansas, thus confirming that people with obesity are vulnerable to COVID-19. Humidity consistently negatively affects COVID-19 across all seasons, denoting that increasing humidity could reduce the risk of COVID-19 infection. In addition, diabetes shows roles in the spread of both early COVID-19 variants and Delta, while humidity plays roles in the spread of Delta and Omicron. Our study highlights the complexity of how multifactor affect COVID-19 in different seasons and counties in Arkansas. These findings are useful for informing local health planning (e.g., vaccine rollout, mask regulation, and testing/tracing) for the residents in Arkansas.

Analisis Perbandingan Dempster Shafer dan Case Base Reasoning Dalam Diagnosis Penyakit Mastocytosis Menggunakan Metode Perbandingan Eksponensial

Diseases pose a major challenge in the medical world. Accurate and precise diagnosis is a crucial key in disease management, including the case of mastocytosis. Mastocytosis is a disorder characterized by the proliferation of mast cells that can affect various organ systems in the human body. There are various variations of mastocytosis, but they can generally be categorized into two main types: cutaneous mastocytosis, which primarily affects the skin and is the most common form, and systemic mastocytosis, which is a more severe form involving the accumulation of mast cells in organs and tissues throughout the body, not just the skin. Planning a successful course of therapy and providing a positive outlook for patients heavily relies on an accurate and timely diagnosis of mastocytosis. This study utilizes an exponential comparison strategy to compare and contrast the Dempster-Shafer (DS) and Case Based Reasoning (CBR) techniques in detecting mastocytosis. The Dempster-Shafer technique is employed in this study to gather and evaluate data from various ambiguous and inadequate sources. This method utilizes the theory of mass variability to represent uncertainty in disease diagnosis. On the other hand, Case Based Reasoning is an approach that relies on past experiences and utilizes existing knowledge in the form of previously encountered cases to make diagnoses. In this study, exponential comparison is used to assign greater weight to certain attributes considered more important in the diagnosis process. This exponential comparison method allows for more flexible attribute weighting, enabling more accurate modeling of symptoms and related factors in mastocytosis. The aim of this research is to use the exponential comparison approach to develop, analyze, and compare both strategies in detecting mastocytosis. With the lowest rate for cutaneous mastocytosis and the highest percentage for systemic mastocytosis, the research findings indicate that both approaches yield comparable results. The Dempster-Shafer approach has proven to be more accurate in mastocytosis diagnosis when comparing the calculations of these two methods with the exponential comparison method

Modeling and assessment of human and organization factors of nuclear safety culture in Saudi Arabia

Saudi Arabia is planning to introduce a nuclear option into its energy mix. The Saudi National Atomic Energy project aims to adopt peaceful nuclear energy. A culture of nuclear safety is an essential element when it comes to constructing and operating nuclear power plants. Several studies have shown how workers adopting a safety culture differs from region to region. However, there is no available research study on the nuclear safety culture in Saudi Arabia. This paper aims to build a framework on what factors apply to nuclear safety culture and what is affected by it. This research used structural equation modeling based on a survey of 240 participants in Saudi Arabia from a wide range of sectors working with radioactive materials in the country. The data was validated before the analysis took place. A high-order construct was used to deeply analyze the data by evaluating five hypotheses. The research concluded that a nuclear safety culture significantly affects personal safety commitment, personal error behaviors, and violation behavior. Three dimensions, individual, management, and environment, directly affect a nuclear safety culture. Policymakers can use this study to evaluate the nuclear safety culture in Saudi Arabia.