Model Validation Research Articles

Development and Validation of a Machine Learning Model for Detection and Classification of Vertigo.

This study aims to investigate whether artificial intelligence can improve the diagnostic accuracy of vertigo related diseases. Based on the clinical guidelines, clinical symptoms and laboratory test results were extracted from electronic medical records as variables. These variables were then input into a machine learning diagnostic model for classification and diagnosis. This study encompasses two primary objectives: Task 1 to distinguish between patients with Benign Paroxysmal Positional Vertigo (BPPV) and non-BPPV. In Task 2, further classifying non-BPPV patients into Ménière's Disease (MD), Vestibular Migraine (VM), and Sudden Sensorineural Hearing Loss accompanied by Vertigo (SSNHLV). The sensitivity, precision, and area under the curve (AUC) metric is primarily used to assess the performance of the machine learning model development phase in a prospective validation cohort. In our study, 1789 patients were recruited as the training cohort and 1148 patients as the prospective validation cohort. The comprehensive diagnostic performance of the XGBoost model surpasses that of traditional models. The sensitivity, accuracy, and AUC in task 1 were 98.32%, 87.03%, and 0.947, respectively. In task 2, the sensitivity values for MD, SSNHLV, and VM were 89.00%, 100.0%, and 79.40%, respectively. The precision values were 88.80%, 100.0%, and 80.00%, respectively. The AUC values were 0.933, 1.000, and 0.931, respectively. The model can significantly improve the accuracy of diagnosing vertigo diseases. This system may enhance the accuracy of classification and diagnosis of vertigo diseases. It offers initial therapy or referrals to clinical doctors, particularly in resource-limited settings. N/A Laryngoscope, 2024.

Sound insulation prediction and optimization of wooden support structure for high-speed train floor based on machine learning

In order to improve the sound insulation performance of high-speed train floors, this study first obtained the necessary data for model training based on the reverberation test method, and then conducted data sorting and feature selection. Next, the maximum mutual information minimum redundancy (mRMR) feature selection algorithm was used to calculate the selected features and screen out a subset of significant features. Subsequently, the decision tree, BP neural network, and support vector machine regression (SVR) methods were applied in sequence, and the standardized feature data were used for the high-speed train floor under the same evaluation criteria of the mean square error (MSE) and coefficient of determination (R2). We conducted training and validation of the sound insulation prediction models for timber-framed support structures. The prediction accuracy of the trained model was compared and evaluated with the finite element statistical energy analysis (FE-SEA) prediction model. Finally, the SVR model was used to optimize the design under constraint conditions. The research results show that based on the research object, sample library, and model training in this article, compared with the FE-SEA model, the prediction error of the SVR model is only 0.3 dB, showing better performance. In engineering practice, the SVR model can effectively optimize the wooden support structure in the floor under certain constraints, and it predicts that the weighted sound insulation of the entire floor is 50.45 dB, which has important engineering application value.

Validation of prognostic models predicting mortality or ICU admission in patients with COVID-19 in low- and middle-income countries: a global individual participant data meta-analysis

BackgroundWe evaluated the performance of prognostic models for predicting mortality or ICU admission in hospitalized patients with COVID-19 in the World Health Organization (WHO) Global Clinical Platform, a repository of individual-level clinical data of patients hospitalized with COVID-19, including in low- and middle-income countries (LMICs).MethodsWe identified eligible multivariable prognostic models for predicting overall mortality and ICU admission during hospital stay in patients with confirmed or suspected COVID-19 from a living review of COVID-19 prediction models. These models were evaluated using data contributed to the WHO Global Clinical Platform for COVID-19 from nine LMICs (Burkina Faso, Cameroon, Democratic Republic of Congo, Guinea, India, Niger, Nigeria, Zambia, and Zimbabwe). Model performance was assessed in terms of discrimination and calibration.ResultsOut of 144 eligible models, 140 were excluded due to a high risk of bias, predictors unavailable in LIMCs, or insufficient model description. Among 11,338 participants, the remaining models showed good discrimination for predicting in-hospital mortality (3 models), with areas under the curve (AUCs) ranging between 0.76 (95% CI 0.71–0.81) and 0.84 (95% CI 0.77–0.89). An AUC of 0.74 (95% CI 0.70–0.78) was found for predicting ICU admission risk (one model). All models showed signs of miscalibration and overfitting, with extensive heterogeneity between countries.ConclusionsAmong the available COVID-19 prognostic models, only a few could be validated on data collected from LMICs, mainly due to limited predictor availability. Despite their discriminative ability, selected models for mortality prediction or ICU admission showed varying and suboptimal calibration.

Analysis and optimization of the influence of bearing assembly parameters on the dynamic characteristics of shaft system

Purpose Inadequate support stiffness leads to motor vibrations exceeding the standard during the commissioning process. However, in-depth research on the parameters affecting bearing support stiffness remains incomplete. This paper aims to reveal the impact of the bearing support stiffness on the shaft system and explore the bearing assembly factors affecting the bearing support stiffness and optimization. Design/methodology/approach The finite-element method is adopted to calculate the bearing support stiffness accurately, with model validation conducted via a test rig. The significant factors affecting bearing housing stiffness are investigated by using the orthogonal experiment method. Finally, a multi-objective optimization strategy for bearing assembly parameters is proposed to improve the bearing support stiffness. Findings The bearing housing stiffness is anisotropic in vertical and horizontal directions, influencing the dynamics of shaft system. Bearing housing looseness can significantly reduce the bearing support stiffness. The contact angle and interference have a very significant effect on the bearing housing stiffness. Preferred combinations of bearing assembly parameters can be obtained by multi-objective genetic algorithms. Originality/value This study proposed a test determination method of bearing housing stiffness, showed that the phenomenon of bearing housing loosening in the test will reduce the bearing support stiffness and considered the respective phase anisotropy of the bearing housing stiffness. The influence of bearing support stiffness on the dynamic characteristics of the shaft system was studied, alongside the optimization of bearing assembly parameters affecting housing stiffness. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0265/

Investigation of the Hottel–Whillier–Bliss Model Applied for an Evacuated Tube Solar Collector

The goal of this research is to examine the applicability of the Hottel–Whillier–Bliss model, developed for flat-plate collectors, to evacuated tube solar collectors. During this study, the model is applied to an evacuated tube collector, and then the identification and validation of the model are made with the help of measurements performed on the collector. This research also includes the application, identification and validation of the energy balance model for the investigated solar collector. This model works for both flat-plate and evacuated tube collectors. The results obtained with the two different models are then compared. By comparing the modelled results with the measured values, the accuracy and applicability of the models can be determined. Based on the results, the Hottel–Whillier–Bliss model works excellently with evacuated tube solar collectors for predicting the outlet temperature of the medium from the solar collector. It is important to note that the identification gives negative heat transfer parameter values. According to the validation, the average absolute error is 0.8 °C, and the average relative error is 1%. For the energy balance model, these values are 0.87 °C and 1.1% respectively, indicating that the accuracy of the Hottel–Whillier–Bliss model is very similar, and even slightly higher. Additionally, the research provides further proof of the applicability of the energy balance model to evacuated tube collectors.

Development, validation and recalibration of a prediction model for prediabetes: an EHR and NHANES-based study

BackgroundA prediction model that estimates the risk of elevated glycated hemoglobin (HbA1c) was developed from electronic health record (EHR) data to identify adult patients at risk for prediabetes who may otherwise go undetected. We aimed to assess the internal performance of a new penalized regression model using the same EHR data and compare it to the previously developed stepdown approximation for predicting HbA1c ≥ 5.7%, the cut-off for prediabetes. Additionally, we sought to externally validate and recalibrate the approximation model using 2017–2020 pre-pandemic National Health and Nutrition Examination Survey (NHANES) data.MethodsWe developed logistic regression models using EHR data through two approaches: the Least Absolute Shrinkage and Selection Operator (LASSO) and stepdown approximation. Internal validation was performed using the bootstrap method, with internal performance evaluated by the Brier score, C-statistic, calibration intercept and slope, and the integrated calibration index. We externally validated the approximation model by applying original model coefficients to NHANES, and we examined the approximation model’s performance after recalibration in NHANES.ResultsThe EHR cohort included 22,635 patients, with 26% identified as having prediabetes. Both the LASSO and approximation models demonstrated similar discrimination in the EHR cohort, with optimism-corrected C-statistics of 0.760 and 0.763, respectively. The LASSO model included 23 predictor variables, while the approximation model contained 8. Among the 2,348 NHANES participants who met the inclusion criteria, 30.1% had prediabetes. External validation of the LASSO model was not possible due to the unavailability of some predictor variables. The approximation model discriminated well in the NHANES dataset, achieving a C-statistic of 0.787.ConclusionThe approximation method demonstrated comparable performance to LASSO in the EHR development cohort, making it a viable option for healthcare organizations with limited resources to collect a comprehensive set of candidate predictor variables. NHANES data may be suitable for externally validating a clinical prediction model developed with EHR data to assess generalizability to a nationally representative sample, depending on the model’s intended use and the alignment of predictor variable definitions with those used in the model’s original development.

Modeling inactivation of non-proteolytic Clostridium botulinum type B spores in a plant-based fish alternative

Our study aims to assess the thermal inactivation of non-proteolytic type B Clostridium botulinum spores in a plant-based fish and to evaluate the potential of alternative heat treatments at temperatures below the safe harbor guidelines established for vacuum-packed chilled products of extended durability. First, the heat resistance of the spore suspension was determined using capillary tubes in potassium phosphate buffer at 80°C. The D80 value was estimated to be 0.7–0.8 min. Then, inactivation was studied in a plant-based fish alternative using “thermal cells equipment.” Inactivation kinetics were obtained at four temperatures: 78, 81, 84 and 85°C, in duplicates. A secondary model describing log10D values versus temperatures was fitted to the dataset. The model parameters ZT and log10Dref (log10D at Tref 82°C) were estimated to be 8.02 ± 0.46°C and 0.32 ± 0.02, respectively. Model validation was done first with additional data collected at three different temperatures (79.1, 82.5, 87.5°C) and second with literature data. The time required to deliver 6 log reduction in the plant-based food matrix was predicted at temperatures within the range 80–90°C. The recommended processing for vacuum-packed chilled products, 90°C for 10 min, was evaluated. We demonstrated that the recommended processing is approximately five times more than the time required for 6 log reduction of non-proteolytic C. botulinum in the plant-based fish alternative, indicating a substantial margin of safety. Our findings highlight the importance of conducting product-specific studies for the evaluation of thermal processing and the potential of process optimization for certain product categories.

Development and Validation of a Multimodal Machine Learning Model for Diagnosing and Assessing Risk of Crohn's Disease in Patients With Perianal Fistula.

Diagnosis of Crohn's disease (CD) can pose challenges, particularly when perianal fistula is the initial presentation. To develop and validate a predictive model, establishing a visual web tool for early diagnosis of CD in patients presenting with perianal fistula. This retrospective, multicentre validation study involved patients diagnosed with either perianal fistulising CD or cryptoglandular fistula who underwent initial perianal fistula surgery subsequent to rectal MRI at three Chinese centres from September 2016 to December 2020. A random forest classification model was trained on the derivation cohort (n = 550), randomly split into training and test sets at a 7:3 ratio. Validation utilised data from two external centres (n = 300). Model interpretation employed the Shapley Addictive explanation (SHAP) framework. The validated model was integrated into a web tool for calculating patient-specific risk. In the derivation cohort, SHAP analysis highlighted rectal wall ulceration, rectal wall thickening, submucosal fistula, and T2 hyperintensity as risk factors, while age was identified as protective. A random forest classification model developed using these top 5 features achieved an AUROC of 0.9425 (95% CI: 0.8943-0.9906). In the validation cohort, the model performed well with AUROC values of 0.9187 (95% CI: 0.8620-0.9754) and 0.9341 (95% CI: 0.8876-0.9806), respectively. We developed a publicly accessible web-based application. We have developed a multimodal machine learning model and a web tool that can predict and present CD risk in patients initially presenting with perianal fistula.

Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease

BackgroundTo investigate the risk factors for readmission of elderly patients with coronary artery disease, and to construct and validate a predictive model for readmission risk of elderly patients with coronary artery disease within 3 years by applying machine learning method.MethodsWe selected 575 elderly patients with CHD admitted to the Affiliated Lu’an Hospital of Anhui Medical University from January 2020 to January 2023. Based on whether patients were readmitted within 3 years, they were divided into two groups: those readmitted within 3 years (215 patients) and those not readmitted within 3 years (360 patients). Lasso regression and multivariate logistic regression were used to compare the predictive value of these models. XGBoost, LR, RF, KNN and DT algorithms were used to build prediction models for readmission risk. ROC curves and calibration plots were used to evaluate the prediction performance of the model. For external validation, 143 patients who were admitted between February and June 2023 from a different associated hospital in Lu'an City were also used.ResultsThe XGBoost model demonstrated the most accurate prediction performance out of the five machine learning techniques. Diabetes, Red blood cell distribution width (RDW), and Triglyceride glucose-body mass index (TyG-BMI), as determined by Lasso regression and multivariate logistic regression. Calibration plot analysis demonstrated that the XGBoost model maintained strong calibration performance across both training and testing datasets, with calibration curves closely aligning with the ideal curve. This alignment signifies a high level of concordance between predicted probabilities and observed event rates. Additionally, decision curve analysis highlighted that both decision trees and XGBoost models achieved higher net benefits within the majority of threshold ranges, emphasizing their significant potential in clinical decision-making processes. The XGBoost model's area under the ROC curve (AUC) reached 0.903, while the external validation dataset yielded an AUC of 0.891, further validating the model's predictive accuracy and its ability to generalize across different datasets.ConclusionTyG-BMI, RDW, and diabetes mellitus at the time of admission are the factors affecting readmission of elderly patients with coronary artery disease, and the model constructed based on the XGBoost algorithm for readmission risk prediction has good predictive efficacy, which can provide guidance for identifying high-risk patients and timely intervention strategies.

Development and validation of a Prediction Model for Chronic Thromboembolic Pulmonary Disease

BackgroundAcute pulmonary embolism (APE) is a critical disease with a high mortality rate, some of the surviving patients may develop chronic thromboembolic pulmonary disease (CTEPD), which affects the patient’s prognosis. However, the research on the early diagnosis of CTEPD is limited. This study aimed to establish a prediction model for earlier identification of CTEPD.MethodsThis prospective study included 464 consecutive patients with APE confirmed between January 2020 and September 2023, at 7 centers from China. After follow-up for at least 3 months, the patients were divided into the CTEPD and non-CTEPD groups based on symptoms and computed tomography pulmonary angiography (CTPA) or pulmonary ventilation perfusion (V/Q) scans showing residual thrombosis. The independent risk factors for CTEPD were identified via univariate and multivariate logistic regression analyses. Next, a nomogram of predictive model was established, and validation was completed via decision curve analysis (DCA) and receiver operating characteristic curve analysis.ResultIn total, 130 (28%) patients presented with CTEPD, 17% (22/130) of CTEPD patients developed chronic thromboembolic pulmonary hypertension (CTEPH). Based on the multivariate analysis, a time interval from symptoms onset to diagnosis (time-to-diagnosis) ≥ 15 days (95% confidence interval [CI]: 3.392–14.972, p < 0.001), recurrent pulmonary embolism (RPE) (95%CI: 1.560–17.300, p = 0.007), right ventricular dysfunction (RVD) (95%CI: 1.042–6.437, p = 0.040), central embolus (95%CI: 1.776–7.383, p < 0.001) and residual pulmonary vascular obstruction (RPVO) > 10% (95%CI: 4.884–21.449, p < 0.001) were identified as the independent predictors of CTEPD. Then, A prediction model with a C-index of 0.895 (95% CI 0.863–0.927) was established for high-risk patients. The nomogram had an excellent predictive performance for earlier identification of CTEPD, with an area under the curve of 0.908 (95%CI: 0.875–0.941) in the training cohort and 0.875 (95%CI: 0.803–0.947) in the validation cohort.ConclusionThe current study established and validated a reliable nomogram for predicting CTEPD, which would assist clinicians identify the high-risk patients for CTEPD earlier.

Validation of a didactic model evaluating the usability, usefulness and acceptability of psychological first aid teaching through simulation

PurposeTo evaluate the effectiveness of simulation as a means to teach Psychological First Aid in Operations (PFA-O). We propose to use a model built on research in didactics to assess the factors which may or may not encourage trainers to adopt and embrace this teaching technique.Material and methodWe submitted a questionnaire to all PFA-O trainers working in France, comprising 15 criteria divided into three dimensions: usability, usefulness and acceptability. Correlation and regression analyses were applied to assess the links between these criteria, teacher motivation, and actual implementation of training courses. We also wanted to test the relevance of these criteria to predict motivation in a multiple linear regression model.Results59 trainers, i.e. 52.68% of the national workforce, responded to the questionnaire. Correlation analysis showed good model consistency, with significant correlations between the 3 dimensions under evaluation (usability, usefulness and acceptability), between these dimensions and teacher motivation, and a strong correlation between teacher motivation and the number of introductory training sessions undertaken each year. The final model incorporating the 15 criteria showed an R2 coefficient of determination of 0.71 (i.e. 71%, F = 4.91, p < 0.001).ConclusionThis didactic model is relevant to assessing the motivational factors which contribute to trainers’ adherence to medical simulation and provides a methodological basis to evaluate the ergonomics of an educational tool in the field of health care. These findings suggest its applicability to a wider range of educational contexts beyond the military.

A framework for validating efficiency models of thermal separation columns with tray internals

Abstract Tray columns are globally used for distillation processes, and their performance must be optimized to reduce their cost and energy expenditures as well as carbon emissions. A prerequisite to achieve these targets demands a realistic account of the tray performance based on tray efficiency model application. The proof of concept of a Refined Residence Time Distribution (RRTD) model, recently proposed by the authors, is presented. The multifaceted challenges in acquiring hydrodynamic and performance data suitable for model validation are discussed in this work. In that regard, an unprecedented experimental campaign was performed in a representative large-scale air-water tray column simulator based on the application of multiplex flow profiler, new chemical system, and novel data processing schemes. Using these data and additional case studies, the capabilities of the RRTD model were demonstrated. This model successfully accounted for the impacts of varying local liquid flow characteristics and vapor flow maldistributions on the tray efficiency thereby advancing the state of the art of the tray efficiency models. This work particularly aimed at proposing a constructive framework to evaluate tray efficiency models in the future campaigns, and those campaigns will benefit from the learnings of the present work.

Reassessment of the Conventional: Numerical Validation of a 1D model for the Prediction of Volute Losses

Abstract The traditional one-dimensional (1D) method for predicting losses in a centrifugal compressor volute relies on simple assumptions. However, it is questionable how accurately these assumptions capture the complex flow physics. This study evaluates these 1D predictions using a comprehensive database of CFD calculations for a standalone volute. In these simulations, the diffuser width and inlet conditions were varied to separately analyze the effects of flow angle and flow rate, allowing for a complete assessment of the volute's loss map. Similar to the 1D predictions, the CFD-predicted total pressure losses in the volute's two subcomponents (the scroll and the conical diffuser) are assigned to the respective velocity components at the volute inlet. This allows for validation of the 1D model against CFD results across the volute's entire operating range. The results show that the radial velocity loss coefficient varies significantly, challenging the 1D assumption of a constant value. In contrast, the tangential velocity loss coefficient in the scroll aligns well with CFD results, showing high loss coefficients at high matching coefficients and decreasing towards low matching coefficients. Losses in the conical diffuser depend on both the matching coefficient and the inlet flow angle, contrary to 1D model assumptions. While the 1D model satisfactorily predicts the global loss coefficient characteristics, it generally overestimates losses. Additionally, the CFD results reveal that the matching coefficient for minimum global losses varies with the flow angle, a factor not considered by the 1D model, indicating its limitations for volute matching assessment.

Hydrological Modeling of Stream Drainage Basins: A Case Study on the Magyaregregy Experimental Catchment in Hungary

This paper presents the field measurements, observations, and numerical simulations conducted for a case study of the Magyaregregy experimental catchment in Hungary. Field measurements included the determination of surface runoff and infiltration intensity on an experimental plot and hydrograph measurements that assessed the ratio between surface and subsurface runoff. Soil moisture measurements both during the infiltration experiments and throughout the experimental catchments gave valuable information regarding this critical parameter. A digital terrain model and the aforementioned field measurements allowed the establishment of a numerical model using HEC-HMS 4.3 for the Magyaregregy experimental catchment process. Although the calibration process was straightforward, considerable difficulties were encountered during the model validation. While the calibration procedure gave appropriate numerical values for most calibrated parameters, it did not provide the proper initial conditions. As a possible solution, the validation period was preceded by a simulation of a relatively long time duration to gain appropriate initial conditions. Finally, the hydrological model’s validation reproduced the measured base flow, as well as the maximum values of discharges. Furthermore, the use of composite-corrected radar data for precipitation values proved to be somewhat unreliable. This supports the principle that data from remote sensing (e.g., radar data) should be used with the utmost care and deliberation as input for hydrological models.

OC6 Phase IV: Validation of CFD Models for Stiesdal TetraSpar Floating Offshore Wind Platform

ABSTRACTWith only a few floating offshore wind turbine (FOWT) farms deployed anywhere in the world, FOWT technology is still in its infancy, building on a modicum of real‐world experience to advance the nascent industry. To support further development, engineers rely heavily on modeling tools to accurately portray the behavior of these complex systems under realistic environmental conditions. This reliance creates a need for verification and validation of such tools to improve reliability of load and dynamic response prediction and analysis capabilities of FOWT systems. The Offshore Code Comparison Collaboration, Continued with Correlation and unCertainty (OC6) project was created under the framework of the International Energy Agency to address this need and considers a three‐sided verification and validation between engineering level models, computational fluid dynamics (CFD), and experimental results. In this paper, a novel floating offshore wind platform, the Stiesdal TetraSpar, is simulated using CFD under the load conditions defined by Phase IV of the OC6 project. The comparison of these CFD results against the experimental results demonstrated the ability to predict the platform response to waves when imposing the measured wave signals as input. Although validation versus experiment was largely successful, the damping behavior was impacted by uncertainties likely originating from the mooring system and sensor umbilical cable. This extensive comparison effort with multiple CFD practitioners offers insight into best practices to achieve reliable results.

Six years of high-resolution monitoring data of 40 borehole heat exchangers

Ground-source heat pumps (GSHPs) coupled with borehole heat exchangers (BHEs) are energy-efficient technologies for heating and cooling buildings. However, these systems often fail to operate at their full potential due to discrepancies between the assumptions made during the design phase and the actual conditions during operation. To enhance overall GSHP performance, it is crucial to collect and analyze long-term monitoring data from operating BHE fields. To our knowledge, no long-term, high-resolution dataset of double U-tube BHEs is currently publicly available. Additionally, most studies typically monitor only the inlet and outlet of the entire ground heat exchanger rather than individual BHEs, hindering detailed performance analysis. With this data descriptor, we present a 6-year dataset from a BHE field comprising 40 BHEs, each with sensors for volume flow and inlet/outlet temperatures, recorded every 30 seconds. We believe this dataset will enhance understanding of individual BHE performance, provide validation for BHE models, and thus support better GSHP design and operation.

Development of an early warning system to reduce the impact of floods related to glacial lake outburst floods (SAGAZ)

Abstract. The retreat of glaciers has led to increasing in natural hazards due, for example, by emptying of lakes dammed by unconsolidated glacial deposits (moraines) or ice, which are susceptible to catastrophic erosion, generating rapid floods, phenomena known as “Glacial Lake Outburst Floods” (GLOFs). Current systems that warn of the occurrence of a GLOF are activated when the emptying begins, which leaves little time to act, so they should be considered early alarm systems. The challenge is to predict the onset of the flood allowing the generation of an early warning system. To address this problem, SAGAZ ultimately aims to develop a system capable of identifying periods of increased GLOF risk using a predictive model fed by weather forecasts and monitoring station data. This system identifies a period of higher risk, which allows informing authorities several days in advance. This paper presents the results of the first phase of SAGAZ implementation, which aimed to (1) develop and validate a prototype monitoring station and deploy a network of stations on glacial lakes across southern Patagonia, (2) collect the necessary data for the development, training and validation of predictive models and (3) begin the implementation and testing of the predictive model. As a result, a network of 10 monitoring stations was installed in the Aysén and Magallanes regions of Chile and 1 in the Province of Santa Cruz in Argentina, of which 6 are currently operational and transmitting data in real time. The rest went off due to power failures and icebergs damaging sensors. The measures we have taken to avoid station’s failures are described, as well as some characteristics of the implemented prototype, the installed networks and the data obtained so far.

Determinants of Small Business Tax Compliance Behaviour: Evidence From Zimbabwean Small Businesses

Many developing countries have implemented tax regulations for small enterprises and the simplification of tax systems in recent times. Tax compliance attitudes within this sector vary significantly due to the high cost to comply and the technical nature of tax law. Many different factors influence presumptive tax compliance and previous research did not necessarily identify the determinants of this phenomenon. Two hundred and nineteen small business owners/managers participated in the study. The validation of the measurement model and the structural model was performed using the Partial-Least Squares Structural Equation Model. The study evaluated the impact of tax morale, corruption and taxpayer/tax office relationships on presumptive tax compliance in the Zimbabwean context. It was found that corruption significantly influences small business operators’ compliance levels under a presumptive tax system. Significant differences were found in small business owners and managers who differed in their perceptions of their relationships with the tax office. The results of this study would assist tax authorities in understanding small businesses’ perceptions when reviewing the current presumptive tax systems. JEL classification: H20, H29 Article History: Received: June 28, 2024; Reviewed: September 5, 2024; Accepted: November 11, 2024; Available online: December 17, 2024.

Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation

Abstract Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological and virological data, integrating different data sources. We propose a novel combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across countries simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales - local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.

Construction and validation of a nomogram model for predicting diabetic peripheral neuropathy

ObjectiveDiabetic peripheral neuropathy (DPN) is a chronic complication of diabetes that can potentially escalate into ulceration, amputation and other severe consequences. The aim of this study was to construct and validate a predictive nomogram model for assessing the risk of DPN development among diabetic patients, thereby facilitating the early identification of high-risk DPN individuals and mitigating the incidence of severe outcomes.Methods1185 patients were included in this study from June 2020 to June 2023. All patients underwent peripheral nerve function assessments, of which 801 were diagnosed with DPN. Patients were randomly divided into a training set (n =711) and a validation set (n = 474) with a ratio of 6:4. The least absolute shrinkage and selection operator (LASSO) logistic regression analysis was performed to identify independent risk factors and develop a simple nomogram. Subsequently, the discrimination and clinical value of the nomogram was extensively validated using receiver operating characteristic (ROC) curves, calibration curves and clinical decision curve analyses (DCA).ResultsFollowing LASSO regression analysis, a nomogram model for predicting the risk of DPN was eventually established based on 7 factors: age (OR = 1.02, 95%CI: 1.01 - 1.03), hip circumference (HC, OR = 0.94, 95%CI: 0.92 – 0.97), fasting plasma glucose (FPG, OR = 1.06, 95%CI: 1.01 - 1.11), fasting C-peptide (FCP, OR = 0.66, 95%CI: 0.56 - 0.77), 2 hour postprandial C-peptide (PCP, OR = 0.78, 95%CI: 0.72 – 0.84), albumin (ALB, OR = 0.90, 95%CI: 0.87 – 0.94) and blood urea nitrogen (BUN, OR = 1.08, 95%CI: 1.01 - 1.17). The areas under the curves (AUC) of the nomogram were 0.703 (95% CI 0.664-0.743) and 0.704 (95% CI 0.652-0.756) in the training and validation sets, respectively. The Hosmer–Lemeshow test and calibration curves revealed high consistency between the predicted and actual results of the nomogram. DCA demonstrated that the nomogram was valuable in clinical practice.ConclusionsThe DPN nomogram prediction model, containing 7 significant variables, has exhibited excellent performance. Its generalization to clinical practice could potentially help in the early detection and prompt intervention for high-risk DPN patients.