A navigational risk evaluation of ferry transport: Continuous risk management matrix based on fuzzy Best-Worst Method.

Ferry transport has witnessed numerous fatal accidents due to unsafe navigation; thus, it is of paramount importance to mitigate risks and enhance safety measures in ferry navigation. This paper aims to evaluate the navigational risk of ferry transport by a continuous risk management matrix (CRMM) based on the fuzzy Best-Worst Method (BMW). Its originalities include developing CRMM to figure out the risk level of risk factors (RFs) for ferry transport and adopting fuzzy BWM to estimate the probability and severity weights vector of RFs. Empirical results show that twenty RFs for ferry navigation are divided into four zones corresponding to their risk values, including extreme-risk, high-risk, medium-risk, and low-risk areas. Particularly, results identify three extreme-risk RFs: inadequate evacuation and emergency response features, marine traffic congestion, and insufficient training on navigational regulations. The proposed research model can provide a methodological reference to the pertinent studies regarding risk management and multiple-criteria decision analysis (MCDA).

Enhancing Emergency Department Management: A Data-Driven Approach to Detect and Predict Surge Persistence.

The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77-8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises.

Investigation and quantification of composition variability in urinary stone analysis.

PURPOSE: To investigate the variability in urinary stone composition analysis due to sampling and suggest potential solutions. MATERIALS AND METHODS: We collected 1,135 stone fragments from 149 instances that had undergone a stone removal at Hanoi Medical University Hospital from January 2022 to August 2022. Each fragment was ground into fine powder and divided into separate specimens if the amount was abundant. For composition analyzing every specimen, Fourier transform infrared spectroscopy was performed. The composition of a given fragment was the average of its belonging specimens. The variability in composition was assessed on the fragment level (i.e., between fragments of an instance). We defined an instance as "significantly variable" if the maximum difference in any composition across its belonging fragments was equal to or greater than a given threshold. RESULTS: On average, there were 7.6±3.3 stone fragments per instance and 2.3±0.5 specimens per fragment. We found that the variability could be substantial on the fragment level. Eighty-nine (69.5%) and 70 (54.7%) out of 128 multiple-component instances were significantly variable if the threshold was set at 20% and 30%, respectively. The variability of an instance on the fragment level was correlated with the size of fragment and the number of components. CONCLUSIONS: Our study demonstrated the significant variability in urinary stone composition and showed that it correlated with the size and the impurity of samples. Mapping denotation while sampling and analyzing as well as reporting the composition of individual fragments could be valuable to reduce potential variability.

Therapists' perspective on acceptance of robot-assisted physical rehabilitation in a middle-income country: a study from Vietnam.

Robot-assisted physical rehabilitation offers promising benefits for patients, yet its adoption among therapists remains a complex challenge. This study investigates the acceptance of robot-assisted physical rehabilitation technology among therapists in Vietnam, a middle-income country with a growing demand for rehabilitation services. Drawing on the Technology Acceptance Model 2 (TAM2) and the Theory of Planned Behaviour (TPB), an online survey and semi-structured interviews were conducted to explore therapists' attitudes and intentions towards using this technology. The results show that Vietnamese therapists recognised its potential benefits and expressed a willingness to use it. Although having similar acceptance patterns compared to developed regions, they demonstrated significantly higher levels of agreement across acceptance constructs. This may be attributed to factors such as the novelty effect, cultural perceptions of robots, and the high workload of therapists in Vietnam. Gender and location were found to influence two acceptance constructs-subjective norms and image, respectively-highlighting the need for tailored strategies in technology implementation. The study underscores the importance of considering socio-cultural factors in the adoption of technology and provides insights for enhancing the acceptance and effectiveness of robot-assisted physical rehabilitation in Vietnam. This contributes to the global understanding of therapist acceptance of technology in this field.

While robot-assisted physical rehabilitation offers promising benefits, there is limited understanding of therapist acceptance on a global scale, highlighting the need for more research in this area.This study in a middle-income country, Vietnam, reveals a generally positive view among therapists, but specific issues such as the novelty effect, cultural perceptions of robots, and high therapist workload impact acceptance levels, indicating the need for tailored strategies.Strategies for implementing robot-assisted physical rehabilitation should include addressing training needs, providing technological support, and considering sociocultural factors to enhance acceptance and effectiveness.

Coarse-Grained Approach to Simulate Signatures of Excitation Energy Transfer in Two-Dimensional Electronic Spectroscopy of Large Molecular Systems.

Two-dimensional electronic spectroscopy (2DES) has proven to be a highly effective technique in studying the properties of excited states and the process of excitation energy transfer in complex molecular assemblies, particularly in biological light-harvesting systems. However, the accurate simulation of 2DES for large systems still poses a challenge because of the heavy computational demands it entails. In an effort to overcome this limitation, we devised a coarse-grained 2DES method. This method encompasses the treatment of the entire system by dividing it into distinct weakly coupled segments, which are assumed to communicate predominantly through incoherent exciton transfer. We first demonstrate the efficiency of this method through simulation on a model dimer system, which demonstrates a marked improvement in calculation efficiency, with results that exhibit good concordance with reference spectra calculated with less approximate methods. Additionally, the application of this method to the light-harvesting antenna 2 (LH2) complex of purple bacteria showcases its advantages, accuracy, and limitations. Furthermore, simulating the anisotropy decay in LH2 induced by energy transfer and its comparison with experiments confirm that the method is capable of accurately describing dynamical processes in a biologically relevant system. This method presented lends itself to an extension that accounts for the effect of intrasegment relaxation processes on the 2DES spectra, which for computational efficiency are ignored in the implementation reported here. It is envisioned that the method will be employed in the future to accurately and efficiently calculate 2D spectra of more extensive systems, such as photosynthetic supercomplexes.

Biomarkers in the prediction of complications in pregnancy after assisted reproductive technology.

OBJECTIVES: To compare the temporal changes in mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), placental growth factor (PlGF), and soluble fms-like tyrosine kinase-1 (sFlt-1) across gestation between assisted reproductive technology (ART) pregnancies complicated with great obstetrical syndromes (GOS) or gestational diabetes (GDM) ± large-for-gestational-age (LGA) fetus, and uncomplicated ART pregnancies. METHODS: This was a prospective longitudinal study of 143 women with singleton pregnancies who conceived through ART at the Department of Obstetrics and Gynecology, Prince of Wales Hospital, the Chinese University of Hong Kong, Hong Kong SAR between December 2017 and January 2020. The participants were followed up at 6-6+3, 11-13+6, 20-24+6, 30-34+6, and 35-37+6 weeks for the measurement of MAP, UtA-PI, PlGF, and sFlt-1. A linear mixed-effects analysis was performed to compare the biomarkers in the GOS, GDM ± LGA, and uncomplicated groups across gestation. RESULTS: Thirty-three (23.1%) and fifty-five (31.5%) women were diagnosed with GOS and GDM ± LGA, respectively. The GOS group had higher estimated marginal mean log10 MAP mulitples of the median (MoM) across gestation, compared with the uncomplicated group (0.00771 vs -0.02022; P < 0.001), when adjusting for clinical visits and days of embryo transfer. The absolute mean log10 MAP MoM in the GOS group was found to be significantly higher than that of the uncomplicated group at all clinical visits from 6 weeks onwards. Furthermore, the estimated marginal mean log10 PlGF MoM was significantly lower in the GOS group across gestation, compared with the uncomplicated group (-0.04226 vs 0.05566; P = 0.010). The significant difference in log10 PlGF MoM was observed from 11-13+6 to 30-34+6 week of gestation (P < 0.05). However, no significant differences in the estimated marginal means of log10 UtA-PI MoM and log10 sFlt-1 MoM between GOS and uncomplicated groups were observed. GDM ± LGA group had a lower estimated marginal mean log10 PlGF MoM throughout pregnancy compared with the uncomplicated group (-0.01536 vs 0.05572; P = 0.032). In the individual visit analysis, the significant difference was observed at the 20-24+6 and 35-37+6 weeks visits (P < 0.05). There were no significant differences in estimated marginal mean log10 MoM of MAP, UtA-PI, and sFlt-1 between GDM ± LGA and uncomplicated groups during pregnancy. CONCLUSION: Our study has revealed that among pregnancies conceived through ART, GOS is associated with higher MAP and lower PlGF from early gestation until late third trimester, while GDM ± LGA is associated with lower PlGF during the second half of pregnancy. The same degree of differences in MAP and PlGF persists from early until late gestation in the GOS group and these findings highlight the importance of early screening during the first trimester to identify women who are at risk for developing GOS following ART procedures. Lastly, the potential of PlGF in predicting the development of GDM from the second trimester of pregnancy requires further investigation.

Implementation of First-trimester Screening and Prevention of Preeclampsia: a Stepped Wedge Cluster-randomized Trial in Asia.

BACKGROUND: This trial aimed to assess the efficacy, acceptability and safety of a first-trimester screen-and-prevent strategy for preterm preeclampsia (PE) in Asia. METHODS: Between 1st August 2019 and 28th February 2022, this multicenter stepped wedge cluster randomized trial included maternity/diagnostic units from ten regions in Asia. The trial started with a period where all recruiting centers provided routine antenatal care without study-related intervention. At regular six-week intervals, one cluster was randomized to transit from non-intervention phase to intervention phase. In the intervention phase, women underwent first-trimester screening for preterm PE using a Bayes theorem-based triple-test. High-risk women, with adjusted risk for preterm PE ≥ 1 in 100, received low-dose aspirin from <16 weeks until 36 weeks. RESULTS: Overall, 88.04% (42,897/48,725) of women agreed to undergo first-trimester screening for preterm PE. Among those identified as high-risk in the intervention phase, 82.39% (2,919/3,543) received aspirin prophylaxis. There was no significant difference in the incidence of preterm PE between the intervention and non-intervention phases (adjusted odds ratio [aOR] 1.59; 95% confidence interval [CI] 0.91 to 2.77). However, among high-risk women in the intervention phase, aspirin prophylaxis was significantly associated with a 41% reduction in the incidence of preterm PE (aOR 0.59; 95%CI 0.37 to 0.92). Additionally, it correlated with 54%, 55% and 64% reduction in the incidence of PE with delivery at <34 weeks (aOR 0.46; 95%CI 0.23 to 0.93), spontaneous preterm birth <34 weeks (aOR 0.45; 95%CI 0.22 to 0.92) and perinatal death (aOR 0.34; 95%CI 0.12 to 0.91), respectively. There was no significant between-group difference in the incidence of aspirin-related severe adverse events. CONCLUSIONS: The implementation of the screen-and-prevent strategy for preterm PE is not associated with a significant reduction in the incidence of preterm PE. However, low-dose aspirin effectively reduces the incidence of preterm PE by 41% among high-risk women. The screen-and-prevent strategy for preterm PE is highly accepted by a diverse group of women from various ethnic backgrounds beyond the original population where the strategy was developed. These findings underpin the importance of the widespread implementation of the screen-and-prevent strategy for preterm PE on a global scale.

Effective degradation of tetracycline in aqueous solution by an electro-Fenton process using chemically modified carbon/α-FeOOH as catalyst.

This study applied an electro-Fenton process using chemically modified activated carbon derived from rubber seed shells loaded with α-FeOOH (RSCF) as catalyst to remove tetracycline residues from aquatic environment. Catalyst characteristics were evaluated using SEM, EDS, XRD, and XPS, showing successful insertion of iron onto the activated carbon. The effects of the parameters were investigated, and the highest treatment efficiency was achieved at pH of 3, Fe: H2O2 ratio (w/w) of 500:1, catalyst dose of 1 g/L, initial TCH concentration of 100 mg/L, and electric current of 150 mA, with more than 90% of TCH being eliminated within 30 min. Furthermore, even after five cycles of use, the treatment efficiency remains above 90%. The rate constant is calculated to be 0.218 min-1, with high regression coefficients (R 2 = 0.93). The activation energy (Ea) was found to be 32.2 kJ/mol, indicating that the degradation of TCH was a simple reaction with a low activation energy. These findings showed that the RSCF is a highly efficient and cost-effective catalyst for TCH degradation. Moreover, the use of e-Fenton process has the advantage of high efficiency, low cost thanks to the recyclability of the catalyst, and environmental friendliness thanks to less use of H2O2.

Ultrasonographic placental parameters at 11-13+6 weeks' gestation in the prediction of complications in pregnancy after assisted reproductive technology.

OBJECTIVE: To evaluate the performance of maternal factors, biophysical and biochemical markers at 11-13 + 6 weeks' gestation in the prediction of gestational diabetes mellitus with or without large for gestational age (GDM ± LGA) fetus and great obstetrical syndromes (GOS) among singleton pregnancy following in-vitro fertilisation (IVF)/embryo transfer (ET). MATERIALS AND METHODS: A prospective cohort study was conducted between December 2017 and January 2020 including patients who underwent IVF/ET. Maternal mean arterial pressure (MAP), ultrasound markers including placental volume, vascularisation index (VI), flow index (FI) and vascularisation flow index (VFI), mean uterine artery pulsatility index (mUtPI) and biochemical markers including placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) were measured at 11-13 + 6 weeks' gestation. Logistic regression analysis was performed to determine the significant predictors of complications. RESULTS: Among 123 included pregnancies, 38 (30.9%) had GDM ± LGA fetus and 28 (22.8%) had GOS. The median maternal height and body mass index were significantly higher in women with GDM ± LGA fetus. Multivariate logistic regression analysis demonstrated that in the prediction of GDM ± LGA fetus and GOS, there were significant independent contributions from FI MoM (area under curve (AUROC) of 0.610, 95% CI 0.492-0.727; p = 0.062) and MAP MoM (AUROC of 0.645, 95% CI 0.510-0.779; p = 0.026), respectively. CONCLUSION: FI and MAP are independent predictors for GDM ± LGA fetus and GOS, respectively. However, they have low predictive value. There is a need to identify more specific novel biomarkers in differentiating IVF/ET pregnancies that are at a higher risk of developing complications.

Validation of the first-trimester machine learning model for predicting pre-eclampsia in an Asian population.

OBJECTIVES: To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first-trimester screening for pre-eclampsia in a large Asian population. METHODS: This was a secondary analysis of a multicenter prospective cohort study in 10 935 participants with singleton pregnancies attending for routine pregnancy care at 11-13+6 weeks of gestation in seven regions in Asia between December 2016 and June 2018. We applied the AI+ML model for the first-trimester prediction of preterm pre-eclampsia (<37 weeks), term pre-eclampsia (≥37 weeks), and any pre-eclampsia, which was derived and tested in a cohort of pregnant participants in the UK (Model 1). This model comprises maternal factors with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor (PlGF). The model was further retrained with adjustments for analyzers used for biochemical testing (Model 2). Discrimination was assessed by area under the receiver operating characteristic curve (AUC). The Delong test was used to compare the AUC of Model 1, Model 2, and the Fetal Medicine Foundation (FMF) competing risk model. RESULTS: The predictive performance of Model 1 was significantly lower than that of the FMF competing risk model in the prediction of preterm pre-eclampsia (0.82, 95% confidence interval [CI] 0.77-0.87 vs. 0.86, 95% CI 0.811-0.91, P = 0.019), term pre-eclampsia (0.75, 95% CI 0.71-0.80 vs. 0.79, 95% CI 0.75-0.83, P = 0.006), and any pre-eclampsia (0.78, 95% CI 0.74-0.81 vs. 0.82, 95% CI 0.79-0.84, P < 0.001). Following the retraining of the data with adjustments for the PlGF analyzers, the performance of Model 2 for predicting preterm pre-eclampsia, term pre-eclampsia, and any pre-eclampsia was improved with the AUC values increased to 0.84 (95% CI 0.80-0.89), 0.77 (95% CI 0.73-0.81), and 0.80 (95% CI 0.76-0.83), respectively. There were no differences in AUCs between Model 2 and the FMF competing risk model in the prediction of preterm pre-eclampsia (P = 0.135) and term pre-eclampsia (P = 0.084). However, Model 2 was inferior to the FMF competing risk model in predicting any pre-eclampsia (P = 0.024). CONCLUSION: This study has demonstrated that following adjustment for the biochemical marker analyzers, the predictive performance of the AI+ML prediction model for pre-eclampsia in the first trimester was comparable to that of the FMF competing risk model in an Asian population.