The calibration of transfer functions is essential for accurate pavement performance predictions in the PavementME design. Several studies have used the least square approach to calibrate these transfer functions. Least square is a widely used simplistic approach based on certain assumptions. Literature shows that these least square approach assumptions may not apply to the non-normal distributions. This study introduces a new methodology for calibrating the transverse cracking and international roughness index(IRI) models in rigid pavements using maximum likelihood estimation(MLE). Synthetic data for transverse cracking, with and without variability, are generated to illustrate the applicability of MLE using different known probability distributions(exponential,gamma, log-normal, and negative binomial). The approach uses measured data from the Michigan Department of Transportation's(MDOT) pavement management system(PMS) database for 70 jointed plain concrete pavement(JPCP) sections to calibrate and validate transfer functions. The MLE approach is combined with resampling techniques to improve the robustness of calibration coefficients. The results show that the MLE transverse cracking model using the gamma distribution consistently outperforms the least square for synthetic and observed data. For observed data, MLE estimates of parameters produced lower SSE and bias than least squares(e.g., for the transverse cracking model, the SSE values are 3.98 vs. 4.02, and the bias values are 0.00 and-0.41). Although negative binomial distribution is the most suitable fit for the IRI model for MLE, the least square results are slightly better than MLE. The bias values are-0.312 and 0.000 for the MLE and least square methods. Overall, the findings indicate that MLE is a robust method for calibration, especially for non-normally distributed data such as transverse cracking.
Background:Fever of unknown origin(FUO)in developing countries is an important dilemma and further research is needed to elucidate the infectious causes of FUO.Methods:A multi-center study for infectious causes of FUO in lower middle-income countries(LMIC)and lowincome countries(LIC)was conducted between January 1,2018 and January 1,2023.In total,15 participating centers from seven different countries provided the data,which were collected through the Infectious DiseasesInternational Research Initiative platform.Only adult patients with confirmed infection as the cause of FUO were included in the study.The severity parameters were quick Sequential Organ Failure Assessment(qSOFA)≥2,intensive care unit(ICU)admission,vasopressor use,and invasive mechanical ventilation(IMV).Results:A total of 160 patients with infectious FUO were included in the study.Overall,148(92.5%)patients had community-acquired infections and 12(7.5%)had hospital-acquired infections.The most common infectious syndromes were tuberculosis(TB)(n=27,16.9%),infective endocarditis(n=25,15.6%),malaria(n=21,13.1%),brucellosis(n=15,9.4%),and typhoid fever(n=9,5.6%).Plasmodium falciparum,Mycobacterium tuberculosis,Brucellae,Staphylococcus aureus,Salmonella typhi,and Rickettsiae were the leading infectious agents in this study.A total of 56(35.0%)cases had invasive procedures for diagnosis.The mean qSOFA score was 0.76±0.94{median(interquartile range[IQR]):0(0–1)}.ICU admission(n=26,16.2%),vasopressor use(n=14,8.8%),and IMV(n=10,6.3%)were not rare.Overall,38(23.8%)patients had at least one of the severity parameters.The mortality rate was 15(9.4%),and the mortality was attributable to the infection causing FUO in 12(7.5%)patients.Conclusions:In LMIC and LIC,tuberculosis and cardiac infections were the most severe and the leading infections causing FUO.
Hakan ErdemJaffar AAl-TawfiqMaha AbidWissal Ben YahiaGeorge AkafityManar Ezzelarab RamadanFatma AmerAmani El-KholyAtousa HakamifardBilal Ahmad RahimiFarouq DayyabHulya CaskurluReham KhedrMuhammad TahirLysien ZambranoMumtaz Ali KhanAun RazaNagwa Mostafa El-SayedMagdalena BaymakovaAysun YalciYasemin CagUmran ElbahrAamer Ikram
