Akaike Information Criterion Value Research Articles

Risk Assessment of Urban Water and Energy Supply Using Copula Function: A Water–Energy Nexus Approach in an Arid City

Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands.

Effect of chemical modification on hydraulic conductivity of stratified porous media

ABSTRACT Understanding hydraulic conductivity is essential in porous media as it dictates the capacity of fluids to permeate through these substances. This research investigated the impact of chemical modification using fly ash on hydraulic conductivity. The coefficient of hydraulic conductivity along the bedding plane at different angles of orientations was predicted by employing machine learning techniques such as gene expression programming (GEP), artificial neural networks (ANNs), and regression tree (RT), considering various influencing parameters. As the amount of fly ash in the porous medium rises, the hydraulic conductivity decreases. The statistical results demonstrate that the proposed ANN model effectively forecasted the horizontal hydraulic conductivity. It achieved the highest R2 value (0.989), the lowest mean absolute percentage error value (5.772), the lowest scatter index value (0.040), and the lowest Akaike Information Criterion value (−2,359.914) compared to the GEP, RT, and previously established approaches. Moreover, a novel equation has been proposed for determining horizontal hydraulic conductivity, which can be used for any stratified medium, distinguishing this work from previous ones. These findings can inform global practices in mitigating groundwater contamination, enhancing irrigation efficiency, and improving the design of subsurface fluid flow systems, making it a valuable contribution to both academic and practical applications worldwide.

Time-series forecasting of particulate organic carbon on the Sunda Shelf: Comparative performance of the SARIMA and SARIMAX models

Conducting in-depth studies of carbon dynamics in the Southeast Asia’s Sunda Shelf waters is challenging. Particulate organic carbon (POC) is a key component that can be monitored in the region, but ocean assessment requires advanced research and POC monitoring, especially in relation to ocean dynamics and forecasts. Accurate forecasting of POC in marine environments is difficult due to the complexity of oceanic processes and the influence of various environmental factors. Statistical forecasting models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) or SARIMA with Exogenous Factors (SARIMAX) are useful for this purpose. This study compares these two time-series forecasting models to assess the role of exogenous factors in predicting POC variability in Indonesian seas. The models were specified as SARIMA (3, 1, 3)x(2, 0, 0, 60) and SARIMAX (3, 1, 3)x(2, 0, 0, 60). We obtained Akaike Information Criterion (AIC) values of 1237.767 for the SARIMA model and 1207.341 for SARIMAX[exog=SST]. The SARIMA model's validation metrics were 6.94 % for MAPE, 10.80 for RMSE, and 0.65 for the correlation coefficient, outperforming the SARIMAX model. While SARIMAX incorporates additional environmental variables, SARIMA outperforms it based on MAPE, RMSE, and correlation coefficients. Our findings reveal that only sea surface temperature can significantly influence POC forecasts, thus providing a new perspective on oceanic carbon dynamics. From 2022–2030, POC levels are expected to range between 108.3 and 135.9 mg/m³, with a mean value of 120.9±5.4 mg/m³, lower than that observed from 2002 to 2022, which was 118.8±11.9 mg/m³. The highest peak in POC is predicted for the end of 2026 and 2027. The annual trend shows that the highest POC values correspond to the northwest monsoon season, with the lowest during the intermonsoon period.

Comparative Performance of ARIMA and ARCH Models On Time Series of Monthly Libyan Brent Oil Price

Many economic and financial time series exhibit a phenomenon known as heteroscedastic, where the variance of the series changes over time. This research study focuses on financial time series modelling, with special application to modelling the price of Libyan Brent Oil. In particular, the theory of univariate nonlinear time series analysis is explored and applied to the price Libyan of Brent Oil, spanning from January 2000 to December 2010. The data was obtained from Bullent of the Platts Market Wire of Statistics. This study aims to evaluate the performance of ARIMA as a linear model and ARCH as a nonlinear modelling data. Multiple time series models were considered for fitting the data, and the best ARIMA models were selected based on the Akaike Information Criteria (AIC). ARIMA (0, 1, 1), and ARIMA (1, 1, 0) were identified as the best models. After estimating the parameters of the selected models, model checking revealed that these models were not suitable for modeling the data, as they lacked validity according to the test of squared residuals. The goodness of fit was assessed using the AIC, and based on minimum AIC values, the best fit ARCH models were found to be ARIMA (0, 1, 1) - ARCH (1). After estimating the parameters of the selected model, a series of diagnostic and forecast accuracy tests were performed. Based on this model, a twelve-month forecast of the price of Libyan Brent crude was made.

Assessment of heart rate measurements by commercial wearable fitness trackers for early identification of metabolic syndrome risk

Metabolic syndrome increases the risks of cardiovascular diseases, type 2 diabetes, and certain cancers. The early detection of metabolic syndrome is clinically relevant, as it enables timely and targeted interventions. In the current study, we aimed to investigate the association between metabolic syndrome and heart rate measured using wearable devices in a real-world setting and compare this association with that for clinical resting heart rate. Data from 564 middle-aged adults who wore wearable devices for at least 7 days with a minimum daily wear time of 20 h were analyzed. The results showed significantly elevated all-day, sleeping, minimum, and inactive heart rates in men with pre-metabolic or metabolic syndrome compared with those in normal individuals, whereas sleeping heart rate and heart rate dips were significantly increased and decreased, respectively, in women with metabolic syndrome. After adjusting for confounders, every 10-beats-per-minute increment in all-day, sleeping, minimum, and inactive heart rates in men corresponded to odds ratios of 2.80 (95% confidence interval 1.53–5.44), 3.06 (1.57–6.40), 4.21 (1.87–10.47), and 3.09 (1.64–6.29), respectively, for the presence of pre-metabolic or metabolic syndrome. In women, the association was significant only for heart rate dips (odds ratio = 0.49 [95% confidence interval 0.25–0.96] for every 10% increment). Models incorporating inactive or minimum heart rate in men and heart rate dip in women demonstrated better fits, as indicated by lower Akaike information criterion values (170.3 in men and 364.9 in women), compared with models that included clinical resting heart rate (173.4 in men and 369.1 in women). These findings suggest that the heart rate indices obtained from wearable devices may facilitate early identification of metabolic syndrome.

Cosmology in f (R, T) modified gravity : unified dark matter and dark energy model constrained by current observations

The proposed cosmological model deals with modified Chaplygin gas (MCG) in f(R, T) = R + ξ(T) gravity, where R is the Ricci Scalar and T is the trace of energy-momentum tensor. The function ξ(T) is chosen as the linear combination of power law and logarithmic form under flat Friedmann-Lemaitre-Robertson-Walker space-time. The model is compatible with current observational data (Pantheon Type Ia Supernova) and confronts the deceleration and state parameters effectively. The model can predict the Big Rip in future infinity and can also tackle the difficulties related to the fine-tuning and the coincidence problem practically. Further, we have numerically solved the modified Friedmann equations in f(R, T) gravity and also performed a Markov Chain Monte Carlo analysis to obtain the best fit parameters of this current cosmological model. These best parameters are then used to compute the cosmographic parameters, i.e., the deceleration parameter, the jerk parameter and the snap parameter. Significantly, the cosmographic test has given valuable insights into the dynamics of the current cosmological model and also enriched us to understand about the cosmic evolution of the accelerated Universe. Additionally, the Statefinder diagnostics and O m diagnostics have provided deeper insights into the dynamics of the cosmic expansion and also provided information to distinguish between both the cosmological frameworks. Furthermore, these tests also reveal that at late times, the current model goes beyond the phantom region. Again, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) have provided enough support for the current model under consideration, indicating that the present model exhibits a plausible explanation. However, the ΛCDM model has emerged with the lowest AIC value which suggests its relatively superior fit compared to the current model. Finally, our current model aligns well with several recent observations and unveils various intriguing features about the late time accelerated Universe.

FORECASTING CBOE OPENING VALUES: AN EFFECTIVE ARIMA APPROACH WITH TRANSFORMATION AND MODEL DIAGNOSTICS

This study investigates the use of an ARIMA model, coupled with Monte Carlo simulation, to forecast the opening value of a Volatility Index (VIX) time series. The data obtained from the Chicago Board Options Exchange (CBOE) for the years 1992–2019 have been transformed into stationary data using a detrend method and first-order difference. The Augmented Dickey-Fuller (ADF) test is used to ensure the data are adequately transformed. The autocorrelation function (ACF) and partial ACF (PACF) are then used to identify series with serial correlation and determine whether an autoregressive (AR) model is appropriate. Significant moving average (MA) lags are also determined for model identification. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are used to find the best-fit ARIMA model. Among the evaluated ARIMA models, which ranged from a random walk ARIMA(0, 1, 0) to ARIMA(2, 1, 2), the ARIMA(2, 1, 2) model was found to be the most optimal, exhibiting the lowest AIC and BIC values. This model was then used to forecast the opening value for the year 2014, using 2013 data as the real data. The generated ARIMA(2, 1, 2) model demonstrates reasonable alignment with the actual 2014 data, suggesting its potential for forecasting in this context. Additionally, Monte Carlo simulations are employed to assess the model’s robustness by generating a range of potential outcomes, providing a more comprehensive understanding of the uncertainty associated with the forecasts.

Improving analysis of cognitive outcomes in cardiovascular trials using different statistical approaches

BackgroundThe Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) questionnaires are commonly used to measure global cognition in clinical trials. Because these scales are discrete and bounded with ceiling and floor effects and highly skewed, their analysis as continuous outcomes presents challenges. Normality assumptions of linear regression models are usually violated, which may result in failure to detect associations with variables of interest.MethodsAlternative approaches to analyzing the results of these cognitive batteries include transformations (standardization, square root, or log transformation) of the scores in the multivariate linear regression (MLR) model, the use of nonlinear beta-binomial regression (which is not dependent on the assumption of normality), or Tobit regression, which adds a latent variable to account for bounded data. We aim to empirically compare the model performance of all proposed approaches using four large randomized controlled trials (ORIGIN, TRANSCEND, COMPASS, and NAVIGATE-ESUS), and using as metrics the Akaike information criterion (AIC). We also compared the treatment effects for the methods that have the same unit of measure (i.e., untransformed MLR, beta-binomial, and Tobit).ResultsThe beta-binomial consistently demonstrated superior model performance, with the lowest AIC values among nearly all the approaches considered, followed by the MLR with square root and log transformations across all four studies. Notably, in ORIGIN, a substantial AIC reduction was observed when comparing the untransformed MLR to the beta-binomial, whereas other studies had relatively small AIC reductions. The beta-binomial model also resulted in a significant treatment effect in ORIGIN, while the untransformed MLR and Tobit regression showed no significance. The other three studies had similar and insignificant treatment effects among the three approaches.ConclusionWhen analyzing discrete and bounded outcomes, such as cognitive scores, as continuous variables, a beta-binomial regression model improves model performance, avoids spurious significance, and allows for a direct interpretation of the actual cognitive measure.Trials registrationORIGIN (NCT00069784). Registered on October 1, 2003; TRANSCEND (NCT00153101). Registered on September 9, 2005; COMPASS (NCT01776424). Registered on January 24, 2013; NAVIGATE-ESUS (NCT02313909). Registered on December 8, 2014.

Evaluation of lymph node ratios and log odds of positive nodes as prognostic indicators in primary organ malignancy

BackgroundStaging of malignant tumours based on positive lymph node number and not taking into account the actual number of examined lymph nodes during surgery may lead to bias and fallacious results. The ratio of positive and resected lymph nodes (LNR) and log odds of positive nodes (LODDS) emerged as novel prognostic markers in deciding the future course of management better than the parameter pN in predicting cancer-specific survival. We aim to evaluate and compare the prognostic value of pN, LNR and LODDS in common primary organ malignancies like breast, head and neck, gastric and cervical cancers. MethodsThis was an ambidirectional observational study conducted from September 2018 to August 2021 with one year (from 2017 to 2018) for retrospective data collection archived from records. It included patients affected by breast, head and neck, gastric and cervical cancers and were followed up for a period of 3 years from the date of diagnosis to perform a holistic survival analysis. The sample was grouped by American Joint Committee on Cancer N stage, LNR and LODDS, and analysed using Kaplan–Meier and multivariate Cox proportional hazard models. ResultsThe study included 202 breast cancer patients, 85 head and neck cancer patients, 100 gastric cancer patients and 52 cervical cancer patients. Multivariate Cox regressions analysis demonstrated a lower Akaike Information Criterion (AIC) value for LODDS and LNR followed by pN stage. ConclusionThe novel indicators LNR and LODDS better predicted the biological behaviour irrespective of lymph node number and allowed clinicians to correctly stratify patients for better postoperative management.

Nomogram based on burn characteristics and the national early warning score to predict survival in severely burned patients

BackgroundExtensive burns are associated with a high mortality rate. Early prediction and action can reduce mortality. The National Early Warning Score (NEWS) is considered the best early warning score for predicting mortality. However, there has been no assessment conducted on the clinical prognostic significance of NEWS in individuals suffering from severe burns. The objective of this research was to establish a nomogram based on burn characteristics and the NEWS to predict survival in severely burned patients. MethodsA retrospective analysis was performed on 335 patients diagnosed with extensive burns from 2005 to 2021 in the Department of Burn Surgery of XX Hospital. Univariate and multivariate analyses were used to determine independent prognostic factors. A nomogram was developed using these prognostic factors and its internal validity was assessed through bootstrap resampling. ResultsThe results of multivariate analysis showed that the independent factors affecting the prognosis of severe burn patients were age, full-thickness burn, creatinine, absorptive injury, tracheotomy, and the NEWS, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion values of the nomogram demonstrated superior goodness-of-fit in predicting severe burns compared to NEWS, with lower scores (195.21 vs. 201.24; 221.91 vs. 224.12, respectively). The bootstrap-adjusted concordance index (C-index) of the nomogram yielded a higher value of 0.923 (95% CI 0.892–0.953), compared to NEWS which had a C-index of 0.699 (95% CI 0.628–0.770). The calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in the nomogram analysis. Furthermore, decision curve analysis indicated promising clinical utility for the proposed nomogram model. By applying an appropriate cutoff value derived from receiver operating characteristics curve analysis, it was observed that the high-risk group identified by the nomogram exhibited a significantly higher mortality rate than the low-risk group. ConclusionThis study introduces an innovative nomogram that predicts the survival rate of individuals with severe burn injuries by combining clinical attributes and laboratory examinations, demonstrating superior efficacy compared to conventional NEWS systems.

Modelling Environmental Tree Species’ Volume using some selected Skewed Distributions

Ecological requirements' knowledge in determining tree species' distributions is a precondition for sustainable forest management. Tree species in all regions are threatened by climate change but some are more vulnerable than others. Rightly skewed distributions were used to take care of the environmental data set obtained from FRIN using five species which are: Beech Wood, White Afara, Opepe, Afon and Teak. Appropriate statistical tools distributions like descriptive analysis, Akaike Information Criterion (AIC), Goodness of Fit, Probability Functions, Kolmogorov Smirnovt-test, Gamma, Weibull, Log-normal and Beta-weibull were carried out to determine the best distribution for each selected specie in this research. It was established from the distributional pattern of the tree species' volume used in this research that the Gamma distribution was a better fit for the Beech wood with the AIC of 617.21, Beta-weibull distribution was a better _t for the White Afara, Opepe and Teak Species with the AIC values of 580.772, 630.84 and 733.60 respectively while the Weibull distribution was a better fit for the Afon specie with the AIC value of 752.07. Conclusively, the implication of the analysis is that the Beta-weibull distribution described the tree species' volume best among the four distributions used. In line with the findings, it is recommended that Beta-weibull should be the appropriate distribution to model forest specie when it comes to modelling because of its four (4) parameters and its goodness when fitting.

A Combined OCBA–AIC Method for Stochastic Variable Selection in Data Envelopment Analysis

This study introduces a novel approach to enhance variable selection in Data Envelopment Analysis (DEA), especially in stochastic environments where efficiency estimation is inherently complex. To address these challenges, we propose a game cross-DEA model to refine efficiency estimation. Additionally, we integrate the Akaike Information Criterion (AIC) with the Optimal Computing Budget Allocation (OCBA) technique, creating a hybrid method named OCBA–AIC. This innovative method efficiently allocates computational resources for stochastic variable selection. Our numerical analysis indicates that OCBA–AIC surpasses existing methods, achieving a lower AIC value. We also present two real-world case studies that demonstrate the effectiveness of our approach in ranking suppliers and tourism companies under uncertainty by selecting the most suitable partners. This research enriches the understanding of efficiency measurement in DEA and makes a substantial contribution to the field of performance management and decision-making in stochastic contexts.

Using the Montgomery-Koyama-Smith equation to calculate the stomatal area per unit lamina area for 12 Magnoliaceae species.

The Montgomery-Koyama-Smith (MKS) equation predicts that total leaf area per shoot is proportional to the product of the sum of individual leaf widths and maximum individual leaf length, which has been validated for some herbaceous and woody plants. The equation is also predicted to be valid in describing the relationship between the total stomatal area per micrograph (AT) and the product of the sum of individual stomatal widths (denoted as LKS) and maximum individual stomatal length (denoted by WKS) in any particular micrograph. To test the validity of the MKS equation, 69,931 stomata (from 720 stomatal micrographs from 12 Magnoliaceae species) were examined. The area of each stoma was calculated using empirical measurements of stomatal length and width multiplied by a constant. Six equations describing the relationships among AT, LKS, and WKS were compared. The root-mean-square (RMSE) and the Akaike information criterion (AIC) were used to measure the goodness of fit, and the trade-off between the goodness of fit and the structural complexity of each model, respectively. Analyses supported the validity of the MKS equation and the power-law equation AT ∝ (LKS∙WKS)α, where a is a scaling exponent. The estimated values of α at the species level and for the pooled data were all statistically smaller than unity, which did not support the hypothesis that AT ∝ LTS∙WTS. The power-law equation had smaller RMSE and AIC values than the MKS equation for the data from the 12 individual species and the pooled data. These results indicate that AT tends to allometrically scale with LKS∙WKS, and that increases in AT do not keep pace with increases in LTS∙WTS. In addition, using the product of LKS and WKS is better than using only one of the two variables.

308 Isoenergetic reduction of dietary macronutrients reveals distinct fecal metabolomic signatures in lean and obese cats fed at maintenance

Abstract Obesity has become a concerning problem in cats. Increasing evidence demonstrates the utility of metabolomics in identifying disease biomarkers and assessing the effects of nutritional interventions, although limited information exists regarding alterations in fecal metabolites in lean and obese domestic cats in response to different macronutrient content. In the current study, three extruded diets were formulated based on adult maintenance using an isoenergentic approach. These diets were created by adjusting the levels of identical ingredients to achieve a low-protein [LP; protein 28% metabolizable energy (ME), fat 40%ME, NFE 32% ME), a low-fat (LF; protein 42% ME, fat 30% ME, NFE 30% ME), and a low-carbohydrate (LC; protein 36% ME, fat 41% ME, NFE 23% ME] diet. Healthy neutered male adult cats [n = 18; body condition scores (BCS) 4 or 5 (lean, n = 9) and 8 or 9 (obese, n = 9)] were fed each diet for a period of 4 wk in a 3 x 3 Latin square design. Fecal samples from each cat were collected between d 22 and 28 of each treatment period. A total of 67 fecal metabolites in six groups (35 amino acids, 11 fatty acids, 10 sugars and sugar metabolites, 5 alcohols, 3 nitrogenous bases, and 3 others) were analyzed using 1H NMR spectroscopy. To evaluate the impact of body condition, diet and their interaction on each metabolite, proc GLIMMIX covariance structure was selected by the smallest Akaike information criterion value in SAS. The Shapiro-Wilk test was used to check normality and lognormal or beta distribution were applied when appropriate. Bonferroni correction post-hoc test was followed to correct Type I error. Statistical significance was considered at P < 0.05. In lean cats, significantly greater concentrations of fecal proteinogenic and branched-chain amino acids, such as methionine, valine, and L-lysine, as well as pyruvic acid and sugars (P < 0.05) were observed. This suggests that these metabolites may either be more actively fermented by bacteria or not utilized as extensively as in obese cats, potentially contributing to a reduction in obesity status. Regardless of body condition, there is a possibility of an enhanced activity in the tryptophan degradation pathway, leading to the greatest concentrations of fecal tryptophan (P = 0.04) in the LF diet, while the greatest fecal D-galactose (P = 0.04) in the LP diet was likely due to the greater dietary carbohydrate content. These observed results suggested that fecal metabolites were influenced by both body condition and diet; however, body condition had a more substantial impact on metabolite changes in cats, whereas no body condition and diet interaction were noted. Overall, the study indicated distinct fecal metabolic signatures in lean and obese cats subjected to different macronutrient diets and further research is warranted to validate the observed findings. Funding was provided by Champion Petfoods, Natural Sciences and Engineering Council of Canada and Mitacs Accelerate.

Comparative analysis of adolescent hypertension definitions for predicting early-adulthood carotid artery intima-media thickness: Tehran lipid and glucose study.

Definitions of childhood and adolescent hypertension (HTN) do not precisely elucidate the relationship between HTN and cardiovascular outcomes. Carotid intima-media thickness (CIMT), as a substitute for cardiovascular outcomes, enables the early identification of cardiovascular events throughout early adulthood. This study aimed to compare the ability of childhood HTN definitions to predict a high CIMT in early adulthood. This prospective cohort study included 921 individuals aged 10-17 years from the Tehran Lipid and Glucose Study (TLGS). The CIMT was measured after 18 years of follow-up. Participants were categorized into normal blood pressure (BP), high-normal BP, HTN stage 1, and HTN stage 2 groups based on the childhood HTN definitions of the 4th report, European Society of Hypertension (ESH), and American Academy of Pediatrics Clinical Practice Guidelines (AAP-CPG). Akaike's information criterion (AIC) and relative efficiencies (RE) were calculated to compare the ability of each to predict a high CIMT (≥95th percentile) during early adulthood. The highest and lowest prevalence of stage 1 HTN was observed with the AAP-CPG (17.7%) and ESH (8.8%), respectively. Similarly, the highest and lowest prevalence of stage 2 HTN was noted with the AAP-CPG (1.5%) and ESH (0.8%), respectively. According to the RE values, the highest to lowest predictive abilities belonged to the 4th report, ESH, and AAP-CPG, respectively. In all models, the 4th report's pediatric HTN definition had the lowest AIC value and offered the best predictive ability. Among the various definitions of pediatric HTN, the 4th report offered the best ability to predict a high CIMT during early adulthood, followed by the ESH and AAP-CPG. Because the reference population of the 4th report includes overweight, obese, and normal-weight individuals, our findings suggest that excessive adiposity is among the main predictors of early adulthood atherosclerosis risk.

Deciphering prognostic indicators in non-HIV cryptococcal meningitis: Constructing and validating a predictive Nomogram model.

Cryptococcal meningitis (CM) is a well-recognized fungal infection, with substantial mortality in individuals infected with the human immunodeficiency virus (HIV). However, the incidence, risk factors, and outcomes in non-HIV adults remain poorly understood. This study aims to investigate the characteristics and prognostic indicators of CM in non-HIV adult patients, integrating a novel predictive model to guide clinical decision-making. A retrospective cohort of 64 non-HIV adult CM patients, including 51 patients from previous studies and 13 from the First Hospital of Shanxi Medical University, was analyzed. We assessed demographic features, underlying diseases, intracranial pressure, cerebrospinal fluid characteristics, and brain imaging. Using the least absolute shrinkage and selection operator (LASSO) method, and multivariate logistic regression, we identified significant variables and constructed a Nomogram prediction model. The model's calibration, discrimination, and clinical value were evaluated using the Bootstrap method, calibration curve, C index, goodness-of-fit test, receiver operating characteristic (ROC) analysis, and decision curve analysis. Age, brain imaging showing parenchymal involvement, meningeal and ventricular involvement, and previous use of immunosuppressive agents were identified as significant variables. The Nomogram prediction model displayed satisfactory performance with an akaike information criterion (AIC) value of 72.326, C index of 0.723 (0.592-0.854), and area under the curve (AUC) of 0.723, goodness-of-fit test P = 0.995. This study summarizes the clinical and imaging features of adult non-HIV CM and introduces a tailored Nomogram prediction model to aid in patient management. The identification of predictive factors and the development of the nomogram enhance our understanding and capacity to treat this patient population. The insights derived have potential clinical implications, contributing to personalized care and improved patient outcomes.

235. A NOVEL TUMOR STAGING SYSTEM INCORPORATING CN STATUS FOR STRATIFYING EARLY STAGE ESOPHAGEAL SQUAMOUS CELL CARCINOMA PATIENTS AFTER TRIMODAL THERAPY

Abstract Background This study aims to assess the prognostic significance of cN status in early-stage esophageal squamous cell carcinoma (ESCC) patients following neoadjuvant chemoradiotherapy (nCRT) and develop a novel staging model for personalized survival prediction. Methods Patients with ESCC who underwent nCRT and esophagectomy were included. The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were employed to evaluate the validity of various oncological staging systems. A reduction in AIC and BIC values indicates improved predictive capability and accuracy. Results We established a novel staging model by incorporating ypTNM stage and cN status, effectively stratifying ypI ESCC patients. Cox regression analysis showed that only cN status was an independent prognostic factor for both overall survival (p = 0.030) and disease-free survival (p = 0.019) in patients with stage ypI. The newly developed ypTNM-cN staging exhibited a superior alignment with overall survival trends compared to the AJCC 8th ypTNM staging, with a significantly lower AIC of 3143.014 versus 3149.950. This superiority was further supported by a BIC of 3146.605 compared to 3153.541. In terms of disease-free survival outcomes, the emerging ypTNM-cN staging, with AIC and BIC values of 3196.057 and 3199.648, respectively, outperformed the AJCC 8th ypTNM staging, which recorded values of 3203.853 and 3207.444. Conclusion We have devised a novel staging system based on ypTNM stage and cN status to accurately categorize patients with ypI stage. Our ypTNM-cN staging system offers valuable insights into the classification of ypI ESCC and demonstrates reliable classification efficacy for all ESCC patients following nCRT and surgery.

Quantifying the heterogeneity impact of risk factors on regional bicycle crash frequency: A hybrid approach of clustering and random parameter model

The existence of internal and external heterogeneity has been established by numerous studies across various fields, including transportation and safety analysis. The findings from these studies underscore the complexity of crash data and the multifaceted nature of risk factors involved in accidents. However, most studies consider the effects of unobserved heterogeneity from one perspective −- either within clusters (internal) or between clusters (external) −- and do not investigate the biases from both simultaneously on crash frequency analysis. To fill this gap, this study proposes a hybrid approach combining latent class cluster analysis with the random parameter negative binomial regression model (LCA-RPNB) to explore the association between risk factors and bicycle crash frequency. First, the bicycle crash data is categorized into three clusters using LCA based on crash features such as gender, trip purposes, weather, and light conditions. Then, the separated crash frequency models for different clusters and the overall model are developed based on RPNB using regional factors of crash locations as independent variables and the crash frequency of different clusters respectively as dependent variables. The hybrid approach enables a comprehensive examination of internal and external heterogeneities among bicycle crash frequency factors simultaneously. Results suggest that the proposed hybrid approach exhibits superior fitting and predictive performance compared to the model only considers the effects of unobserved heterogeneity from one perspective with the lower values of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This approach can help policymakers and urban planners to design more effective safety interventions by understanding the distinct needs of different bicyclist clusters and the specific factors that contribute to crash risk in each group.

Modelling Petrol Prices in Kenya from 2014 to 2023 Using Sarimax Model: A Case Study of Nairobi County

The requirement for petrol price information is crucial for majority of enterprises. This is because fluctuations in petrol prices impact inflation hence affecting daily lives of citizens. In analyzing the prices of petrol, researchers have employed several models but encountered various limitations. These limitations include; the Error Correction Model can examine only one co-integrating association. The Vector Autoregression (VAR) model does not account for the structural changes in the data. Additionally, the AutoRegressive Integrated Moving Average (ARIMA) model does not take into consideration the seasonal component in the data. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model assumes that over time the volatility is constant. Moreover, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model does not integrate the external factors. Hence in this study Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model was employed since it captures seasonality in data and incorporates the exogenous variables. The research’s aim was to model prices of petrol in Kenya for the period between 2014 to 2023 with exchange rates as an external factor. Secondary data was obtained from Energy and Petroleum Regulatory Authority (EPRA), Kenya National Bureau of Statistics (KNBS) and Central Bank of Kenya (CBK) websites. R software was used to analyze the data. By the use of historical data of petrol prices and exchange rates, the study sought to fit the best Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, validate the model and predict the petrol prices. The petrol price data was found to be non-stationary using Augmented Dickey Fuller test (ADF). Regular differencing was conducted to make the data stationary. Seasonal differencing due to seasonality component available in the data was also performed. Best SARIMAX model was chosen from various SARIMAX models according to Box-Jenkins methodology which uses least Akaike Information Criterion (AIC) value. SARIMAX (0,1,1)(2,1,2)<sub>12</sub> model was selected since it had least Akaike Information Criterion (AIC) value of 656.3733 and the model validated using the hold out technique. The forecasts errors from the training set were; Mean Squared Error (MSE)=10.4970, Root Mean Square Error (RMSE)=3.239911, Mean Absolute Percentage Error (MAPE)=2.309268% while those from the testing set were; Mean Squared Error (MSE)=3271.1012, Root Mean Square Error (RMSE)=57.193542, Mean Absolute Percentage Error (MAPE)=26.695390%. There was less error in the training set than in the testing set as it was expected hence the model suited the data well and could be used for future predictions. The model was then used for five year forecast into the future. This study’s findings will offer sound suggestions to policymakers, businesses and consumers. This study recommends a model to be fitted using other factors affecting petrol prices and fitting Fourier terms, Behavioral Assessment Tools (BATS) and Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) models.

Development and validation of accelerated failure time model for cause-specific survival and prognostication of oral squamous cell carcinoma: SEER data analysis.

Oral Squamous Cell Carcinoma is the most prevalent malignancies affecting the oral cavity. Despite progress in studies and treatment options its outlook remains grim with survival prospects greatly affected by demographic and clinical factors. Precisely predicting survival rates and prognosis plays a role in making treatment choices for the best achievable overall health outcomes. To develop and validate an accelerated failure time model as a predictive model for cause-specific survival and prognosis of Oral Squamous Cell Carcinoma patients and compare its results to the traditional Cox proportional hazard model. We screened Oral cancer patients diagnosed with Squamous Cell Carcinoma from the Surveillance Epidemiology and End Results (SEER) database between 2010 and 2020. An accelerated failure time model using the Type I generalized half logistic distribution was used to determine independent prognostic factors affecting the survival time of patients with oral squamous carcinoma. In addition, accelerated factors were estimated to assess how some variables influence the survival times of the patients. We used the Akaike Information Criterion, Bayesian Information Criterion to evaluate the model fit, the area under the curve for discriminability, Concordance Index (C-index) and Root Mean Square Error and calibration curve for predictability, to compare the type I generalized half logistic survival model to other common classical survival models. All tests are conducted at a 0.05 level of significance. The accelerated failure time models demonstrated superior effectiveness in modeling (fit and predictive accuracy) the cause-specific survival (CSS) of oral squamous cell carcinoma compared to the Cox model. Among the accelerated failure time models considered, the Type I generalized half logistic distribution exhibited the most robust model fit, as evidenced by the lowest Akaike Information Criterion (AIC = 27370) and Bayesian Information Criterion (BIC = 27415) values. This outperformed other parametric models and the Cox Model (AIC = 47019, BIC = 47177). The TIGHLD displayed an AUC of 0.642 for discrimination, surpassing the Cox model (AUC = 0.544). In terms of predictive accuracy, the model achieved the highest concordance index (C-index = 0.780) and the lowest root mean square error (RMSE = 1.209), a notable performance over the Cox model (C-index = 0.336, RMSE = 6.482). All variables under consideration in this study demonstrated significance at the 0.05 level for CSS, except for race and the time span from diagnosis to treatment, in the TIGHLD AFT model. However, differences emerged regarding the significant variations in survival times among subgroups. Finally, the results derived from the model revealed that all significant variables except chemotherapy, all TNM stages and patients with Grade II and III tumor presentations contributed to the deceleration of time to cause-specific deaths. The accelerated failure time model provides a relatively accurate method to predict the prognosis of oral squamous cell carcinoma patients and is recommended over the Cox PH model for its superior predictive capabilities. This study also underscores the importance of using advanced statistical models to improve survival predictions and outcomes for cancer patients.