Nonparametric Methods Research Articles

Severe Uncontrolled Asthma: A Longitudinal Retrospective Study Illustrating the Experience of the Pulmonology Clinic of Târgu-Mureș, Romania

Introduction: Severe uncontrolled asthma (SUA) affects approximately 5% of asthma patients, leading to frequent exacerbations, reduced lung function, and lower quality of life. Recent biologic therapies target specific inflammatory pathways, offering new options for SUA. Objective: This study aimed to evaluate clinical characteristics, treatment outcomes, and biomarkers in patients with SUA treated with biologics (Omalizumab, Benralizumab, and Dupilumab) at our clinic. Material and Methods: A six-month retrospective longitudinal study was conducted on 28 patients aged 36–83 years with SUA. Patients were divided into three groups: Omalizumab (n = 4), Benralizumab (n = 18), and Dupilumab (n = 6). Lung function tests and biomarkers such as eosinophil and IgE levels were measured over 3-month periods (T0, T1, and T2). Asthma control was assessed using asthma control tests (ACT), and non-parametric statistical methods were applied. Results: The median patient age was 64 years, with 75% showing elevated eosinophil counts (>300 cells/µL). Benralizumab significantly improved lung function (p < 0.05) and ACT scores (p < 0.001), reducing eosinophil counts to zero (p < 0.001). Patients on Dupilumab and Omalizumab showed improved asthma control (p < 0.05) and reduced exacerbations, albeit to a lesser extent (p > 0.05). Conclusions: Biologics, particularly Benralizumab and Dupilumab, improved asthma control, lung function, and quality of life in SUA patients, with improved ACT scores and spirometry values. Some patients remained poorly controlled, emphasizing the need for personalized treatment and regular biomarker monitoring. Multidisciplinary management and lifestyle changes are critical for better outcomes in SUA.

Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise

ABSTRACT The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (C ab), maximum carboxylation rate (V cmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and C ab were reliably retrieved in most cases, whereas V cmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field.

Estimating reference intervals from an IPD meta-analysis using quantile regression

BackgroundReference intervals, which define an interval in which a specific proportion of measurements from a healthy population are expected to fall, are commonly used in medical practice. Synthesizing information from multiple studies through meta-analysis can provide a more precise and representative reference interval than one derived from a single study. However, the current approaches for estimating the reference interval from a meta-analysis mainly rely on aggregate data and require parametric distributional assumptions that cannot always be checked.MethodsWith the availability of individual participant data (IPD), non-parametric methods can be used to estimate reference intervals without any distributional assumptions. Furthermore, patient-level covariates can be introduced to estimate personalized reference intervals that may be more applicable to specific patients. This paper introduces quantile regression as a method to estimate the reference interval from an IPD meta-analysis under the fixed effects model.ResultsWe compared several non-parametric bootstrap methods through simulation studies to account for within-study correlation. Under fixed effects model, we recommend keeping the studies fixed and only randomly sampling subjects with replacement within each study. ConclusionWe proposed to use the quantile regression in the IPD meta-analysis to estimate the reference interval. Based on the simulation results, we identify an optimal bootstrap strategy for estimating the uncertainty of the estimated reference interval. An example of liver stiffness measurements, a clinically important diagnostic test without explicitly established reference range in children, is provided to demonstrate the use of quantile regression in estimating both overall and subject-specific reference intervals.

A Unified Framework for Covariate Adjustment Under Stratified Randomisation

ABSTRACTRandomisation, as a key technique in clinical trials, can eliminate sources of bias and produce comparable treatment groups. In randomised experiments, the treatment effect is a parameter of general interest. Researchers have explored the validity of using linear models to estimate the treatment effect and perform covariate adjustment and thus improve the estimation efficiency. However, the relationship between covariates and outcomes is not necessarily linear, and is often intricate. Advances in statistical theory and related computer technology allow us to use nonparametric and machine learning methods to better estimate the relationship between covariates and outcomes and thus obtain further efficiency gains. However, theoretical studies on how to draw valid inferences when using nonparametric and machine learning methods under stratified randomisation are yet to be conducted. In this paper, we discuss a unified framework for covariate adjustment and corresponding statistical inference under stratified randomisation and present a detailed proof of the validity of using local linear kernel‐weighted least squares regression for covariate adjustment in treatment effect estimators as a special case. In the case of high‐dimensional data, we additionally propose an algorithm for statistical inference using machine learning methods under stratified randomisation, which makes use of sample splitting to alleviate the requirements on the asymptotic properties of machine learning methods. Finally, we compare the performances of treatment effect estimators using different machine learning methods by considering various data generation scenarios, to guide practical research.

Joint calibration of local volatility models with stochastic interest rates using semimartingale optimal transport

We develop and implement a non-parametric method for joint exact calibration of a local volatility model and a correlated stochastic short rate model using semimartingale optimal transport. The method relies on the duality results established in Joseph et al. (Calibration of local volatility models with stochastic interest rates using optimal transport, 2023). and jointly calibrates the whole equity-rate dynamics. It uses an iterative approach which starts with a parametric model and tries to stay close to it, until a perfect calibration is obtained. We demonstrate the performance of our approach on market data using European SPX options and European cap interest rate options. Finally, we compare the joint calibration approach with the sequential calibration, in which the short rate model is calibrated first and frozen.

Serum Lipid Profile and Electrolytes Reference Intervals for Apparently Healthy Children and Adolescents in Addis Ababa, Ethiopia.

Accurate reference intervals generated from an apparently healthy population and stratified by crucial variables such as age and gender are required to guarantee appropriate interpretation of test results. Since there were no local reference intervals in the study area, the present study aimed to establish reference intervals on serum lipid profiles and electrolytes for children and adolescents in Addis Ababa. This community-based cross-sectional study was conducted from April to October 2019. Laboratory analysis was performed using the automatic biochemical analyzer Cobas 6000 (c501) from Roche. According to Clinical and Laboratory Standards Institute (CLSI) guidelines, reference intervals for lipid profile and electrolyte tests for apparently healthy children and adolescents were established. We used a non-parametric method to calculate the 2.5th and 97.5th percentiles with a 90% confidence interval. In children, the reference intervals for serum potassium, sodium, chloride, calcium, magnesium, and phosphate in mmol/L were 4.37-5.20, 137-145.50, 101.90-107.90, 2.34-2.70, 0.74-0.97, and 1.42-1.85, respectively; and for total cholesterol, triglycerides, low-density lipoprotein, and high-density lipoprotein, the respective values were 100.76-171.70, 44.16-126.36, 60.60-105.60, and 31.60-53.70 in mg/dL, for both genders. For adolescents, the reference intervals were 4.03-5.58, 137-146, 98.90-120.90, 2.39-2.70, 0.73-0.96, and 0.96-1.80 for serum potassium, sodium, chloride, calcium, magnesium, and phosphate in mmol/L, respectively; and 97.20-189.10, 40.50-143.60, 41.70-120.90, and 21.30-57.0 in mg/dL for total cholesterol, triglycerides, low-density lipoprotein, and high-density lipoprotein, respectively, for both genders. The established reference intervals in the current study revealed that both the lower and upper limits contradicted the manufacturer values as well as the available literature. The study also discovered significant gender differences in reference values for TC, TG, LDL-C, potassium, phosphate, and chloride in the adolescent age groups.

EEG Oscillatory Activity and Resting-State Networks Associated with Neurocognitive Function in Mild Traumatic Brain Injury.

This study aimed to investigate the characteristics of resting-state electroencephalography (EEG) activity and brain networks in patients with mild traumatic brain injury (mTBI) and their association with neurocognitive function (NCF). We analyzed 26 patients with subacute mTBI and 21 healthy controls. The subacute mTBI group (9 females, 17 males) had a mean age of 29.9 ± 9.9 years, and the healthy controls (11 females, 10 males) had a mean age of 29.7 ± 11.5 years. Current source density, lagged phase synchronization, and resting-state network activity were analyzed using exact low-resolution electromagnetic tomography (eLORETA) with 60 s resting-state EEG data. In addition, a correlation analysis was performed between these EEG parameters and NCF in patients with mTBI. We used the statistical nonparametric mapping method in eLORETA to correct for multiple comparisons. There were no significant differences in EEG parameters between the patients with mTBI and healthy controls. However, in patients with mTBI, correlation analysis revealed negative correlations between theta activity in the anterior cingulate cortex and verbal short-term memory and between activity in the memory perception network and verbal memory. Our findings suggest that resting-state EEG may be clinically useful in investigating the mechanism of NCF decline in patients with mTBI.

Comparative analysis of alveolar bone volume reconstruction outcomes using the guided bone regeneration technique

Relevance. The success of dental implant treatment in cases of reduced alveolar bone volume hinges on restoring bone to the required dimensions in each case. The guided bone regeneration (GBR) technique, which encompasses various techniques, holds a central role in oral reconstructive surgery and dental implantology due to its capacity for continuous improvement. The authors' experience with a modified GBR technique incorporating a situational Vicryl framework (SVF) has demonstrated enhanced effectiveness, with the potential to restore vertical bone volume by 5 mm or more. This study aimed to evaluate the advantages of the modified GBR technique using SVF compared to the conventional GBR technique.Materials and methods. This study is based on a comprehensive comparative analysis of two datasets: a retrospective evaluation of dental implant treatment outcomes in 210 patients who required alveolar bone volume restoration using the classical GBR technique, and clinical data from 40 patients treated with both the classical GBR technique and the proposed modified SVF technique The oral status of 40 patients included in the clinical analysis was characterized by partial edentulism, accompanied by mild to moderate alveolar bone deformities resulting from either inflammatory resorption or natural atrophy. The retrospective group 1 (comparisons) consisted of archival data of 210 patients. Retrospective data from 210 patients and clinical data from 40 patients were subjected to statistical analysis using parametric and non-parametric methods with IBM SPSS Statistics software.Results. A comprehensive comparative analysis of retrospective and clinical data revealed the following results. The value of the integrated "restored bone volume" parameter after vertical reconstruction in group 1 was 2-3 mm. The mean value of the integrated "restored bone volume" parameter after vertical reconstruction in group 2 was 3.20±0.96 mm, while in group 3, it was 4.60±1.63 mm for patients of both sexes. Comparison of the integrated "baseline deformation" parameter by technique showed a median deformation of 2.95 mm in group 2, treated with the classical GBR technique, and 4.95 mm in group 3, treated with the modified SVF technique. Similarly, the integrated "restored bone volume" parameter showed a median value of 2.95 mm in group 2 and 4.85 mm in group 3. The average value of the integrated "restored bone volume" parameter in group 2 was comparable to retrospective data from 210 patients in group 1.Conclusion. The comprehensive analysis of alveolar bone volume reconstruction using two GBR techniques, based on "baseline deformation" and "restored bone volume" parameters, demonstrated a significant advantage of the modified GBR technique compared to the classical technique.

Machine learning-based analysis of nutrient and water uptake in hydroponically grown soybeans

Recent advancements in sustainable agriculture have spurred interest in hydroponics as an alternative to conventional farming methods. However, the lack of data-driven approaches in hydroponic growth presents a significant challenge. This study addresses this gap by varying nitrogen, magnesium, and potassium concentrations in hydroponically grown soybeans and conducting essential nutrient profiling across the growth cycle. Statistical techniques like Linear Interpolation are employed to interpolate nutrient data and a feature selection pipeline consisting of chi-squared testing methods, Linear Regression with Recursive Feature Elimination (RFE) and ExtraTreesClassifier have been used to select important nutrients for predicting water uptake using non-parametric regression methods. For different nutrient growth media, i.e. for soybeans grown in Hoagland + Nitrogen and Hoagland + Magnesium media, the Random Forest regressor outperformed other methods in predicting water uptake, achieving testing Mean Squared Error (MSE) scores of 24.55 (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score 0.63) and 8.23 (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score 0.81), respectively. Similarly, for soybeans grown in Hoagland + Potassium media, Support Vector Regression demonstrated superior performance with a testing MSE of 4.37 and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score of 0.85. SHapley Additive exPlanations (SHAP) values are examined in each case to elucidate the contributions of individual nutrients to water uptake predictions. This research aims to provide data-driven insights to optimize hydroponic practices for sustainable food production.

A Reproducibility Focused Meta-Analysis Method for Single-Cell Transcriptomic Case-Control Studies Uncovers Robust Differentially Expressed Genes.

Here we systematically studied the reproducibility of DEGs in previously published Alzheimer's Disease (AD), Parkinson's Disease (PD), and COVID-19 scRNA-seq studies. We found that while transcriptional scores created from differentially expressed genes (DEGs) in individual PD and COVID-19 datasets had moderate predictive power for the case control status of other datasets (mean AUC=0.77 and 0.75, respectively), genes from individual AD datasets had poor predictive power (mean AUC=0.68). We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets. The meta-analysis genes had improved predictive power (AUCs of 0.88, 0.91, and 0.78, respectively). By multiple other metrics, specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. The DEGs revealed known and novel biological pathways, and we validate the BCAT1 gene as down-regulated in oligodendrocytes in an AD mouse model. Our analyses show that for heterogeneous diseases, DEGs of individual studies often have low reproducibility, but combining information across multiple datasets promotes the rigorous discovery of reproducible DEGs.

Modelling of the time to death of breast cancer patients at Hiwot Fana Specialized University Hospital

Breast cancer is the most common cause of cancer death and is a frequently diagnosed cancer among women worldwide. It is becoming a challenging health condition in Ethiopia with a high rate of morbidity and mortality. The main aim of this study was to model the time to death in breast cancer patients at Hiwot Fana Specialized University Hospital. A retrospective cohort study was carried out from April 1st, 2020, to April 1st, 2023, and 296 women were included in the study. We used nonparametric methods and Bayesian accelerated failure time models (with Laplace approximation) to identify risk factors and choose a model fitting breast cancer patient data. Model comparison was performed using the marginal likelihood, deviance information criterion and Watanabe Akaike information criterion. From the total of 296 patients in the study, 56 (18.9%) died. The estimated median survival time was 33 months. The log-rank test showed that age group, stage, alcohol consumption, smoking habit, and comorbidity were potential risk factors associated with the time to death in breast cancer patients at the 5% level of significance. The Bayesian Weibull accelerated failure time model was found to be the best fitted model for predicting the survival time of patients with minimum DIC (520.39) and WAIC (521.59) values. The final Bayesian Weibull AFT model with the integrated nested Laplace approximation estimation technique revealed that age group, stage, alcohol consumption, smoking habit, and comorbidity were significantly associated with the time to death in breast cancer patients. Individuals older than 65 years, with stage IV disease, drinking alcohol, smoking cigarettes and having comorbidities had shortened survival times in patients with breast cancer. Hence, Hiwot Fana Specialized University Hospital and related bodies should work on awareness creation to reduce smoking habits and alcohol use as well as give due attention to elderly and stage IV breast cancer patients during intervention.

Long–period trends in water temperature changes in the northern part of the Atlantic Ocean from ocean reanalysis data

The results of assessing long–period changes in water temperature in the North Atlantic Ocean (0°–70° N, 8°–80° W) based on data from ocean reanalyses and objective analyses for the periods 1961– 2011 and 1980–2011 are presented. The obtained estimates are based on the application of a nonparametric method of regression analysis (quantile regression) to the monthly ocean temperature for a quantile value of 0.5. During the period 1961–2011 warming was mainly observed in the upper 400 m layer in the region from the equator to 70° N. Over this 51-year period, the increase in the median monthly ocean temperature averaged over the analyzed water area ~0.5°C, and in the Gulf Stream–North Atlantic Current system ~1°C. During the period 1980–2011 warming in the North Atlantic Ocean mainly occurred in the upper 1 km layer at high latitudes (50°–65° N). Over this 32-year period, the increase in the median monthly ocean temperature in the subpolar gyre in the upper 400 m layer was ~1°C.

Longitudinal Data Clustering Methods: A Systematic ReviewLongitudinal Data Clustering Methods: A Systematic Review

Introduction: In the last few decades, in many research fields, different methods were introduced to discover groups with the same trends in longitudinal data. The clustering process is an unsupervised learning method, which classifies longitudinal data based on different criteria by performing algorithms. The current study was performed with the aim of reviewing various methods of longitudinal data clustering, including two general categories of non-parametric methods and model-based methods. Methods: In this research, to obtain related scientific articles, PubMed, Science Direct Scopus, ISI, and Google Scholar were searched between 2000 and 2021. Results: According to our systematic review, the non-parametric k-means Clustering Method utilizing Euclidean distance emerges as a leading approach for clustering longitudinal data. Conclusion: This research, with an overview of the studies done in the field of clustering, can help researchers as a toolbox to choose various methods of longitudinal data clustering in idea generation and choosing the appropriate method in the classification and analysis of longitudinal data.

Estimation of the Unemployment Rate in Indonesia Using Nonparametric Spline Regression

The COVID-19 pandemic impact is not only on health but on all aspects of life, one of the socio-economic aspects is unemployment. The linear regression be used as a method in modeling the unemployment rate frequently. Whereas linear regression is inappropriate for data that have trends or irregular relationship patterns such as unemployment rate data. Therefore, this research used of the nonparametric spline regression method. It can produce more flexible curves according to the flatline of data based on optimum knots at the optimum value of the cross validation. There are several factors suggested that affect the Unemployment Rate (TPT ), including the number of positive COVID cases, labor force participation rate, economic growth and human development index. The purpose of this study is to find out if there is a difference between the open unemployment rate (TPT) in Indonesia before and during the pandemic. The Data used are secondary data obtained from Badan Pusat Statistika (BPS). The calculations results show that there are differences between the models of the unemployment rate before and during the pandemic. Generally there are three significant variables in the before the pandemic model: variables of economic growth (PE) and the human development index (IPM), while during the pandemic the significant variable are the labor force participation rate (TPAK) and economic growth (PE).

Short-Term Optimal Operation Method for Hydro–Wind–Thermal Systems Considering Wind Power Uncertainty

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas.

PREDICTION OF UNIT VALUE INDEX OF EXPORTS OF SITC 897 JEWELRY AND PRECIOUS GOODS GROUP IN INDONESIA

Export is an international trade activity that plays an important role in the economic progress in Indonesia. One of Indonesia's leading commodities that dominate the export market is jewelry. In export activities, the export unit value index is an important component that serves to describe the development of export commodity prices. This unit value index always changes every time and fluctuates. This research conducts a comparative analysis of the performance of parametric method, non-parametric method, and machine learning, specifically, ARIMA, Fourier series estimator, and Support Vector Regression (SVR). This study aims to evaluate the effectiveness of various methods in improving prediction accuracy for the unit value index of the SITC code 897 in Indonesia. The research data used is secondary data including monthly export unit value index data with SITC code 897 in Indonesia obtained from the Central Bureau of Statistics. The data divided into 90% training data and 10% testing data. The methods used in this analysis are ARIMA, Fourier series estimator, and SVR. The best model obtained from each method is ARIMA (1,1,1) with MAPE of 10.92%, Fourier series estimator with MAPE of 8.47%, and an SVR RBF kernel function with MAPE of 3.73%. The results of this study obtained the best method for predicting the unit value index of SITC code 897 is SVR with an RMSE value of 8.288 and very good prediction accuracy.

HbA1c comparable to fasting glucose in the external validation of the African Diabetes Risk Score and other established risk prediction models in Black South Africans

BackgroundThe use of non-invasive risk scores to detect undiagnosed type 2 diabetes (T2D) ensures the restriction of invasive and costly blood tests to those most likely to be diagnosed with the disease. This study assessed and compared the performance of the African Diabetes Risk Score (ADRS) with three other diabetes risk prediction models for identifying screen-detected diabetes based on fasting plasma glucose (FPG) or glycated haemoglobin (HBA1c).MethodsAge, sex, waist circumference, body mass index, blood pressure, history of diabetes and physical activity levels from the SA-NW-PURE study were used to externally validate the ADRS and other established risk prediction models. Discrimination was assessed and compared using C-statistics and nonparametric methods. Calibration was assessed using calibration plots, before and after recalibration.ResultsNine hundred and thirty-seven participants were included; 14% had prevalent undiagnosed T2D according to FPG and 26% according to HbA1c. Discrimination was acceptable and was mostly similar between models for both diagnostic measures. The C-statistics for diagnosis by FPG ranged from 0.69 for the Simplified FINDRISC model to 0.77 for the ADRS model and 0.77 for the Simplified FINDRISC model to 0.79 for the ADRS model for diagnosis by HbA1c. Calibration ranged from acceptable to good, though over- and underestimation were present. All models improved significantly following recalibration.ConclusionsThe models performed comparably, with the ADRS offering a non-invasive way to identify up to 79% of cases. Based on its ease of use and performance, the ADRS is recommended for screening for T2D in certain Black population groups in South Africa. HbA1c as a means of diagnosis also showed comparable performance with FPG. Therefore, further validation studies can potentially use HbA1c as the standard to compare to.

Dynamic Analysis of Geared Rotor System with Hybrid Uncertainties

Current research on the dynamics and vibrations of geared rotor systems primarily focuses on deterministic models. However, uncertainties inevitably exist in the gear system, which cause uncertainties in system parameters and subsequently influence the accurate evaluation of system dynamic behavior. In this study, a dynamic model of a geared rotor system with mixed parameters and model uncertainties is proposed. Initially, the dynamic model of the geared rotor-bearing system with deterministic parameters is established using a finite element method. Subsequently, a nonparametric method is introduced to model the hybrid uncertainties in the dynamic model. Deviation coefficients and dispersion parameters are used to reflect the levels of parameter and model uncertainty. For example, the study evaluates the effects of uncertain bearing and mesh stiffness on the vibration responses of a geared rotor system. The results demonstrate that the influence of uncertainty varies among different model types. Model uncertainties have a more significant than parametric uncertainties, whereas hybrid uncertainties increase the nonlinearities and complexities of the system’s dynamic responses. These findings provide valuable insights into understanding the dynamic behavior of geared system with hybrid uncertainties.

A Child-Centred Design Evaluation of a Learning Game to Improve Children’s Legal Capability

Children lack awareness and understanding of their rights and the law. There is a need to address this gap and empower children to become advocates for their rights when they encounter law-related issues. A promising approach to achieving this is through the use of games. In particular, games focused on learning have evolved into novel tools that can raise awareness as well as help children acquire essential skills. In this paper, we present a child-centred design approach to evaluating the initial version of a learning game designed to help children across a wide age range (3-15 years) and with diverse characteristics, improve their legal knowledge, skills, and confidence. This study supports the integral role and value of child participation in testing the usability of a law-based learning game and shaping its design. A mixed methods approach, grounded in user-centred principles, was employed to assess the first prototype (P1) of the game. 45 children from various schools and early years settings in South Yorkshire and the Midlands, UK were involved. This included children from: 1) advisory groups, 2) case study groups, and 3) game development groups, with an age range of 4 to 16 years old. School staff were also invited to give their feedback. Quantitative and qualitative data were gathered through questionnaires, and field note observations, and analyses were conducted through both thematic analysis and non-parametric statistical methods. We found the game was received positively overall, but further development was needed to make it accessible and engaging, especially for the youngest participants. Importantly, participants of diverse ages and characteristics shared valuable insights into enhancements to make the game more intuitive, interactive, and enjoyable, and these findings informed the development of the beta version of the game.

Prognostic impact of diagnostic and therapeutic time delays in breast cancer: an exploratory data analysis for patients at Parirenyatwa Hospital, Zimbabwe

Objective: This paper seeks to investigate factors related to time delays for diagnosis and treatment in breast cancer patients at Parirenyatwa Hospital in Zimbabwe and subsequently evaluate the effects of presentation and diagnosis delays on cancer stage. Methods: The study was done for 379 patients with histologically diagnosed invasive breast cancer, from 2015 through to 2029. The study sought to identify factors associated with the time delays ( months)using parametric and non-parametric methods, depending on whether underlying assumptions of such tests are met. A multiple logistic regression model was also used to analyse the association between factors, primary delay, secondary delay variables and cancer stage. Results: The median of the primary, secondary and treatment delay were found to be 7.6, 1 and 0.4 months respectively. Rural residence, Karnofsky Performance Score below 70%, hypertension comorbidity, tumor size (>5cm) and well differentiated tumors (grade 1) were significant factors for delayed presentation. Longer primary delay times and post-menopausal status were associated with secondary delay. Advanced cancer stage at diagnosis and those on medical aid were more likely to have a delay in treatment onset. Conclusion: Primary and secondary delay were predictive of advanced disease using logistic regression. Keywords: Breast cancer; primary delay; secondary delay; treatment delay; Zimbabwe.