Good Prediction Accuracy Research Articles

Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark

The current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R2), Root Mean Squared Error (RMSE), and Concordance Index (C-index). The Random Forest model achieved the highest prediction accuracy among all machine learning models, followed by Linear Regression and the Decision Trees. The scatter plot for Linear Regression demonstrates good predictive accuracy for mid-range values. However, it shows significant deviations at the extremes, indicating that the model struggles to capture the full range of variability in the data. The bar chart of coefficients pinpoints the variables with the greatest impact on the predictions, providing suggestions for potential areas that can be improved and providing model interpretability. Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. The results greatly impact several sections, including aquaculture management, policy-making, and operational strategies, providing valuable insights for stakeholders and decision-makers. Apache Spark was used for data processing and machine learning model implementation; Apache Cassandra was also used for data storage, ensuring efficient large dataset management and SQL tools for structured data handling; Oracle VM VirtualBox for cross-platform virtualization; and Spark Connector was also used.

An Integrated Nomogram Combining Deep Learning and Radiomics for Predicting Malignancy of Pulmonary Nodules Using CT-Derived Nodules and Adipose Tissue: AMulticenter Study.

Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary nodules. One thousand and ninety-eight patients with 6-30 mm pulmonary nodules who received histopathologic diagnosis at 3 centers were included and divided into a primary cohort (PC), an internal test cohort (I-T), and two external test cohorts (E-T1, E-T2). The DLRCN was built by integrating adipose tissue radiomics features, intranodular and perinodular deep learning features, and clinical characteristics for diagnosing malignancy of pulmonary nodules. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. The performance of DLRCN was assessed with respect to its calibration curve, area under the curve (AUC), and decision curve analysis (DCA). Furthermore, we compared it with three radiologists. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and subgroup analysis were also taken into account. The incorporation of adipose tissue radiomics features led to significant NRI and IDI (NRI = 1.028, p < 0.05, IDI = 0.137, p < 0.05). In the I-T, E-T1, and E-T2, the AUCs of DLRCN were 0.946 (95% CI: 0.936, 0.955), 0.948 (95% CI: 0.933, 0.963) and 0.962 (95% CI: 0.945, 0.979), The calibration curve revealed good predictive accuracy between the actual probability and predicted probability (p > 0.05). DCA showed that the DLRCN was clinically useful. Under equal specificity, the sensitivity of DLRCN increased by 8.6% compared to radiologist assessments. The subgroup analysis conducted on adipose tissue radiomics features further demonstrated their supplementary value in determining the malignancy of pulmonary nodules. The DLRCN demonstrated good performance in predicting the malignancy of pulmonary nodules, which was comparable to radiologist assessments. The adipose tissue radiomics features have notably enhanced the performance of DLRCN.

Developing a neural network model to predict the optimal minimum flow rate for effective hole cleaning

The object of the study is effective well cleaning during drilling. The subject of the study is the development of a machine learning model based on a neural network for predicting the optimal minimum drilling fluid flow rate. The challenge is the need to improve well cleaning efficiency to prevent stuck pipes and the associated downtime and costs. During the study, a neural network model was developed and tested to predict the minimum flow rate for cleaning wells. The model was trained and tested on data showing its high accuracy and reliability. The mean square error (MSE) reached 0.019169 for LSTM and 0.0828 for GRU, indicating the accuracy of the predictions. Neural network architectures such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to efficiently process time series of data and consider long-term dependencies. The results are explained using advanced neural network architectures and machine learning algorithms, which made it possible to achieve good accuracy of predictions. These architectures enable efficient model training on large amounts of data, allowing complex dependencies and influencing factors to be considered. Distinctive features of the results include good accuracy of predictions and the ability to use the model in real-world conditions. The model demonstrates good performance and reliability in predicting the minimum flow rate. The results of the study can be used to optimize the processes of well drilling. Practical applications include using the model to predict the optimal minimum flow rate in various conditions, which will reduce the risks of stuck pipes and increase the efficiency of drilling operations. The model can be integrated into existing monitoring and control systems for drilling processes to improve their performance

Application of preoperative NLR-based prognostic model in predicting prognosis of intrahepatic cholangiocarcinoma following radical surgery

PurposeTo investigate the application value of the neutrophil to lymphocyte count ratio (NLR) in the prognostic analysis of intrahepatic cholangiocarcinoma (ICC) after radical resection, and to offer guidance for the individualized perioperative diagnosis and treatment of ICC.MethodsThe clinical data of 360 patients diagnosed with ICC following radical surgery were retrospectively analyzed. The cut-off value of NLR was calculated using the minimum p-value method, and then divided into High-NLR (H-NLR) group and Low-NLR (L-NLR) group according to the NLR cut-off value. The prognostic value of NLR in ICC was analyzed. Subsequently, the patients were divided into the hepatolithiasis-related intrahepatic cholangiocarcinoma (HICC) group and the non-hepatolithiasis-related intrahepatic cholangiocarcinoma (NHICC) group based on whether they combined with hepatolithiasis. Multiple regression models were constructed based on NLR and clinicopathological indicators to verify the application value of prognostic models in the survival and recurrence of ICC patients after radical surgery.ResultsThe cut-off value of NLR was 2.36, and the survival analysis disclosed that overall ICC patients with NLR ≥ 2.36 manifested a poor 5-year survival rate and a higher tumor recurrence rate (p &amp;lt; 0.001). In the HICC group, patients with H-NLR presented a poor 5-year survival rate and a higher tumor recurrence rate compared with L-NLR (p &amp;lt; 0.001). The NLR-based survival/recurrence prediction models in the HICC group demonstrated excellent predictive capacity (H-L test: 0.359/0.680, AUC: 0.764/0.791). In the NHICC group, patients with H-NLR exhibited a poor 5-year survival rate compared with L-NLR (p &amp;lt; 0.001), yet there was no significant difference in tumor recurrence between the two groups (p = 0.071). The NLR-based survival prediction model in the NHICC group demonstrated acceptable predictive ability (H-L test: 0.268, AUC: 0.729), while the NLR-based recurrence prediction model did not show an effective predictive ability (H-L test: 0.01, AUC: 0.649).ConclusionNLR is an independent risk factor influencing postoperative survival and recurrence in ICC patients, particularly in HICC patients. Preoperative NLR ≥ 2.36 suggests that patients might have a poor prognosis. The survival and recurrence prediction model constructed based on NLR and other clinical indicators demonstrates good prediction accuracy and can effectively predict the risk of postoperative adverse prognosis in patients with HICC. This study offers a novel idea for the clinical treatment of HICC patients.

Prediction of perovskite oxygen vacancies for oxygen electrocatalysis at different temperatures

Efficient catalysts are imperative to accelerate the slow oxygen reaction kinetics for the development of emerging electrochemical energy systems ranging from room-temperature alkaline water electrolysis to high-temperature ceramic fuel cells. In this work, we reveal the role of cationic inductive interactions in predetermining the oxygen vacancy concentrations of 235 cobalt-based and 200 iron-based perovskite catalysts at different temperatures, and this trend can be well predicted from machine learning techniques based on the cationic lattice environment, requiring no heavy computational and experimental inputs. Our results further show that the catalytic activity of the perovskites is strongly correlated with their oxygen vacancy concentration and operating temperatures. We then provide a machine learning-guided route for developing oxygen electrocatalysts suitable for operation at different temperatures with time efficiency and good prediction accuracy.

Prediction of Hospital Readmission Using the CORE and CORE+ Scores in Persons With COPD.

Identifying persons with COPD at high risk for hospital readmission provides opportunities for efficient and appropriate care to lower readmission risk. This study examined 30-d and 60-d hospital readmission prediction of the COPD-readmission (CORE) score and a newly developed CORE+ score. The relationship between CORE and CORE+ scores and ICU admission, endotracheal intubation, and in-hospital noninvasive ventilation (NIV) use was explored. A retrospective cohort study evaluated participants with spirometry-confirmed COPD from 2 Midwestern academic hospitals. The CORE score variables included eosinophil blood count, FEV1/FVC (<0.70) and FEV1 (≤40% of predicted), triple inhaler therapy, previous hospitalization, and presence of neuromuscular disease. Out-of-hospital NIV use and Charlson comorbidity index were added to compose the CORE+ score. Researchers assessed associations between variables and outcomes with chi-square test or Fisher exact test, compared results of CORE and CORE+ scores with Wilcoxon signed-rank test, assessed each score's 30-d and 60-d readmission predictive power with multiple logistic regression, and evaluated predictive accuracy with AUC of receiver operating characteristic using alpha < 0.05. Of 391 participants, the study found a 22% 30-d, all-cause readmission rate and a 16% 60-d, all-cause readmission rate. CORE+ score had better predictive accuracy than the CORE score for 30-d readmission (area under the curve 0.81 [95% CI 0.76-0.86]; AUC 0.73 [95% CI 0.66-0.79], P < .001) and 60-d readmission (AUC 0.77 [95% CI 0.71-0.83]; AUC 0.75 [95% CI 0.69-0.81], P < .001). Participants who used in-hospital NIV had higher median CORE+ scores (P = < .001). CORE and CORE+ scores demonstrated good to very good predictive accuracy for 30-d and 60-d readmission, respectively. Moreover, this study demonstrated a linear relationship between in-hospital NIV use and CORE+ score.

PREDICTING PATIENT OUTCOMES USING ONE-HOUR ADULT EARLY WARNING SCORE (AEWS)

Early Warning Scores (EWS) are widely utilized tools to predict patient outcomes, particularly in critical care settings and emergency departments (ED). However, while Adult Early Warning Scores (AEWS) have been employed for general patient outcome prediction, the specific utility of a one-hour AEWS in the ED has not been thoroughly explored. Accurate early prediction of patient disposition could enhance clinical decision-making, resource allocation, and patient safety. Objective: This study aims to evaluate the effectiveness of one-hour Adult Early Warning Scores (AEWS) in predicting patient disposition (discharge, floor admission, or ICU admission) in the emergency department. Methods: A prospective observational study was conducted on 227 patients aged 16 years and above who presented to the ED of Shifa International Hospital, Islamabad, from January 2024 to June 2024. Vital signs, including heart rate, respiratory rate, blood pressure, oxygen saturation, and temperature, were recorded at the one-hour mark post-admission, and the one-hour AEWS was calculated. Patient outcomes were categorized into three groups: discharge, admission to the general floor, or admission to the ICU. Logistic regression was used to determine the relationship between one-hour AEWS and patient disposition. Receiver Operating Characteristic (ROC) curve analysis was performed to assess the predictive power of the AEWS for determining patient outcomes. Results: There was a statistically significant correlation between the one-hour AEWS and patient disposition (p&lt;0.001). The ROC curve analysis showed an Area Under the Curve (AUC) of 0.753, demonstrating good predictive accuracy. A cutoff AEWS value of 2.5 was identified, with patients scoring below this threshold being more likely to be discharged, while those scoring above 2.5 were more likely to require hospital admission or ICU care. The sensitivity and specificity of AEWS at this cutoff were 71% and 68%, respectively. Conclusion: The one-hour AEWS has proven to be a valuable tool in predicting patient disposition in the emergency department. Its ability to distinguish between patients who can be safely discharged and those requiring further care highlights its potential utility in improving patient triage, optimizing resource use, and enhancing clinical decision-making. Further multicenter studies are needed to validate these findings and assess the integration of AEWS into routine ED practice.

Prognostic nomograms for young breast cancer: A retrospective study based on the SEER and METABRIC databases.

Young breast cancer (YBC) is a subset of breast cancer that is often more aggressive, but less is known about its prognosis. In this study, we aimed to generate nomograms to predict the overall survival (OS) and breast cancer-specific survival (BCSS) of YBC patients. Data of women diagnosed with YBC between 2010 and 2020 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated into a training cohort (n = 15,227) and internal validation cohort (n = 6,526) at a 7:3 ratio. With the Cox regression models, significant prognostic factors were identified and used to construct 3-, 5-, and 10-year nomograms of OS and BCSS. Data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used as an external validation cohort (n = 90). We constructed nomograms incorporating 10 prognostic factors for OS and BCSS. These nomograms demonstrated strong predictive accuracy for OS and BCSS in the training cohort, with C-indexes of 0.806 and 0.813, respectively. The calibration curves verified that the nomograms have good prediction accuracy. Decision curve analysis demonstrated their practical clinical value for predicting YBC patient survival rates. Additionally, we provided dynamic nomograms to improve the operability of the results. The risk stratification ability assessment also showed that the OS and BCSS rates of the low-risk group were significantly better than those of the high-risk group. Here, we generated and validated more comprehensive and accurate OS and BCSS nomograms than models previously developed for YBC. These nomograms can help clinicians evaluate patient prognosis and make clinical decisions.

Identification and validation of M2 macrophage-related gene signature as a novel prognostic model for head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor. Commonly used tumor staging don’t sufficiently and accurately assess the prognosis of HNSCC patients, resulting in a lack of guidance for clinical treatment decisions. M2 macrophage infiltration has been shown to be strongly associated with the tumor prognosis. In this study, we used the Cancer Genome Atlas (TCGA) data to screen for genes co-expressed with M2 macrophages in HNSCC. We used univariate Cox regression to screen out the genes associated with HNSCC prognosis, and constructed a HNSCC prognosis model by Lasso regression analysis. The results confirmed that the model had good predictive value and accuracy for the prognosis of HNSCC patients by survival analysis, ROC curve and nomogram. We divided the HNSCC samples into high-risk and low-risk groups according to the risk score, and the results showed that patients in the high-risk group were more prone to genetic mutations and had a higher tumor mutational burden. In addition, there were significant differences between risk groups in terms of immune cell infiltration and drug sensitivity. The HNSCC prognostic model established in this study may provide guidance for clinical therapeutic decision-making and provide a theoretical foundation for the development of new immunotherapy methods.

Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration

BackgroundCurrent predictive machine learning techniques for spontaneous preterm birth heavily rely on a history of previous preterm birth and/or costly techniques such as fetal fibronectin and ultrasound measurement of cervical length to the disadvantage of those considered at low risk and/or those who have no access to more expensive screening tools.Aims and objectivesWe aimed to develop a predictive model for spontaneous preterm delivery < 37 weeks using socio-demographic and clinical data readily available at booking -an approach which could be suitable for all women regardless of their previous obstetric history.MethodsWe developed a logistic regression model using seven feature variables derived from maternal socio-demographic and obstetric history from a preterm birth (n = 917) and a matched full-term (n = 100) cohort in 2018 and 2020 at a tertiary obstetric unit in the UK. A three-fold cross-validation technique was applied with subsets for data training and testing in Python® (version 3.8) using the most predictive factors. The model performance was then compared to the previously published predictive algorithms.ResultsThe retrospective model showed good predictive accuracy with an AUC of 0.76 (95% CI: 0.71–0.83) for spontaneous preterm birth, with a sensitivity and specificity of 0.71 (95% CI: 0.66–0.76) and 0.78 (95% CI: 0.63–0.88) respectively based on seven variables: maternal age, BMI, ethnicity, smoking, gestational type, substance misuse and parity/obstetric history.ConclusionPending further validation, our observations suggest that key maternal demographic features, incorporated into a traditional mathematical model, have promising predictive utility for spontaneous preterm birth in pregnant women in our region without the need for cervical length and/or fetal fibronectin.

Evaluation of a Risk Model for Sepsis Early Prediction in Infants with Epidermolysis Bullosa

Abstract Objective Epidermolysis bullosa (EB) is a severe hereditary condition characterized by fragile skin that can lead to complications, including severe infections such as sepsis. Current research on sepsis in children with EB is limited, and there is a need for specific biomarkers that can aid in early detection and management. Methods This study analyzed blood samples from 92 children diagnosed with EB, 42 of whom developed sepsis. We investigated various inflammatory proteins and clinical parameters as potential biomarkers. Multivariate analysis was used to determine predictors of sepsis occurrence. Results Elevated levels of C-reactive protein (CRP), interleukin-6 (IL-6), lactate, and decreased oxygen saturation were significantly associated with sepsis in children with EB. The predictive model displayed an area under the receiver operating characteristic curve of 0.80 in the training set and 0.77 in the validation set, indicating good predictive accuracy. Conclusion Our findings suggest that CRP, IL-6, lactate, and oxygen saturation are reliable predictors of sepsis in children with EB. These biomarkers should be monitored closely to facilitate early diagnosis and improve outcomes in this vulnerable population. The study underscores the need for tailored research and diagnostic strategies for children with EB at risk of sepsis.

Groundwater potential and recharge zone mapping using GIS and remote sensing techniques: the Melka Kunture Watershed in Ethiopia

Groundwater is the most dynamic natural resource that is not uniformly distributed both in space and time. Identifying it for sustainable water resource development for domestic use, irrigation, and industrial purposes is the biggest challenge and key concern due to climate change, overexploitation, and a lack of proper management. Geographical information system (GIS) and remote sensing (RS) play a fundamental role in identifying suitable groundwater potential and recharge zones. In this study, groundwater potential and recharge zone map are delineated using GIS and RS techniques integrated with Analytic Hierarchy Process (AHP) for the Melka Kunture Watershed in Ethiopia. The thematic layers used were: Lithology, geomorphology, slope, lineament density, land use/cover, rainfall, drainage density and soil. GIS has been used to integrate and overlay thematic layers using a weighted overlay analysis tool, whereas the AHP method has been used to assign the weights, normalize, and rank thematic layers and their associated sub-themes based on their suitability, characteristics, or influence on groundwater potential and recharge zones. The output groundwater potential zone map is categorized into four suitable zones: high potential cover 1105.60 km2 (25.70%), moderate potential 2308.00 km2 (53.73%), low potential 880.0 km2 (20.49%), and very low potential 47.80 km2 (1.10%), whereas there are three suitable groundwater recharge potential zones: high potential cover 250.20 km2 (5.80%), moderate recharge 3647.90 km2 (84.00%), and low recharge 443.50 km2 (10.20%) of the study area. Hence, the study areas have been more suitable for groundwater potential and recharge zones as compared to the total area. The groundwater potential map was validated using the existing water source and well points, and it indicated a good prediction accuracy of 88%. Thus, the delineated groundwater potential and recharge zone maps are reliable, and the resultant potentiality mapping has played a vital role in the sustainable development and management of the water resources in the study area.

Silent Diabetes: Key Risk Factors Among the Low-Income Population of Langkawi Island, Kedah, Malaysia (2022-2023).

Diabetes mellitus (DM) poses a growing global health challenge, contributing to significant morbidity, mortality, and economic burden. In Malaysia, the prevalence of diabetes is increasing, especially among low-income populations with limited access to healthcare. Many cases remain undiagnosed, increasing the risk of severe complications and further straining healthcare resources. Island populations, such as those in Langkawi, are particularly vulnerable due to geographical isolation, socioeconomic constraints, and inadequate healthcare services. To determine the prevalence of undiagnosed DM and identify the associated risk factors among the low-income population on Langkawi Island, Kedah, Malaysia. We conducted a cross-sectional study from January 2022 to December 2023, involving 1,070 participants aged 40 years and above, all eligible under the low-income scheme. Data on sociodemographic characteristics, body mass index (BMI), lifestyle, and medical history were collected through a structured proforma from four local health clinics. Logistic regression analysis was used to identify significant predictors of undiagnosed DM. The model's predictive accuracy was assessed using the area under the receiver operating characteristic (ROC) curve. The prevalence of undiagnosed DM among the low-income population on Langkawi Island was 6.7%. Multiple logistic regression found three important predictors: having a higher BMI (overweight: adjusted odds ratio (AOR): 2.72; 95% confidence interval (CI): 1.40-5.30; p = 0.003; obese: AOR: 2.43; 95% CI: 1.19-5.00; p = 0.015); living on a smaller island (AOR: 1.71; 95% CI: 1.03-2.85; p = 0.039); and having a medical history (AOR: 0.21; 95% CI: 0.12-0.36; p < 0.001). The model demonstrated good predictive accuracy with an area under the ROC curve of 0.758 and correctly classified 93.3% of cases. This study reveals a significant burden of undiagnosed DM within Langkawi's low-income population, especially among individuals with higher BMI and those residing in geographically isolated areas. The findings highlight the urgent need for enhanced, context-specific screening programsand early detection efforts tailored to this vulnerable population. Effective public health strategies should prioritize regular health check-ups, obesity management, and improved access to healthcare services in isolated communities to reduce the prevalence and complications associated with undiagnosed diabetes.

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

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

INTEGRATED FORECASTING AND AGGREGATE PLANNING FOR PRODUCTION OPTIMIZATION: A COMPARATIVE ANALYSIS OF OVERTIME AND SUBCONTRACTING CONTROLS

The current potential market demand with ever-changing situations and conditions must be managed properly to find out the potential market demand in the future. Rumah Warna Yogyakarta is one of the manufacturing industry players that has experienced fluctuations in market demand, even tending to decline from the end of 2021 to mid-2022. Data was obtained from the database and direct interviews with Rumah Warna in Yogyakarta from November 2021 to November 2022. This study aims to determine the prediction of product demand for Rumah Warna Yogyakarta in the next period, so that companies can carry out production planning strategies to minimize production cost. Product demand prediction is carried out using the Grey System method of the GM (1,1) model. Then proceed with the heuristic aggregate planning method that focuses on overtime control and subcontracting control. Based on the results of the analysis, the Grey System GM (1,1) method produces good prediction accuracy of 9.231%. The best aggregate planning method is the overtime control where Rumah Warna Yogyakarta can reduce costs by Rp 351,258,758 when compared to the subcontracting control method.

Structural evolution and stabilisation mechanism of foam fluids that contain cellulose nanofibres and cellulose nanocrystals: An experimental and theoretical study

Foam fluids are thermodynamically metastable systems, and they evolve over timescales comparable to their time of use, which makes the study of their destabilisation mechanisms crucial for many industrial applications. In our study, foams were made from fluids that contained cellulose nanocrystals (CNCs) and cellulose nanofibrils (CNFs). We explored the effects of nanocelluloses and oils (n-heptane and n-dodecane) on foam stability, bubble coarsening and liquid drainage. Results show that the evolution of the bubble size is strongly slowed by the synergistic effect of CNCs and CNFs, and the structural evolution of foam is greatly impacted. The main responsible mechanism is an increase in local yield stresses due to the presence of CNCs in the foam. The jammed CNCs in foam produce a drainage delay, which makes the foam 50 % drainage time (t50%) of the experimental value deviate by up to 400 % from the classical theory value. When the bubbles coarsen and grow above a threshold size, liquid can drain out of the foam. Finally, a modified model for t50% that considers the drainage delay was proposed. The experimental results validated the proposed correlation and exhibited good reliability and predictive accuracy. The results enable us to elucidate the impact of CNCs and CNFs on the stability of foam fluids.

Identification of RTS,S/AS01 vaccine-induced humoral biomarkers predictive of protection against controlled human malaria infection.

BACKGROUNDThe mechanism(s) responsible for the efficacy of WHO-recommended malaria vaccine RTS,S/AS01 are not completely understood. We previously identified RTS,S vaccine-induced Plasmodium falciparum circumsporozoite protein-specific (PfCSP-specific) antibody measures associated with protection from controlled human malaria infection (CHMI). Here, we tested the protection-predicting capability of these measures in independent CHMI studies.METHODSVaccine-induced total serum antibody (immunoglobulins, Igs) and subclass antibody (IgG1 and IgG3) responses were measured by biolayer interferometry and the binding antibody multiplex assay, respectively. Immune responses were compared between protected and nonprotected vaccinees using univariate and multivariate logistic regression.RESULTSBlinded prediction analysis showed that 5 antibody binding measures, including magnitude-avidity composite of serum Ig specific for PfCSP, major NANP repeats and N-terminal junction, and PfCSP- and NANP-specific IgG1 subclass magnitude, had good prediction accuracy (area under the receiver operating characteristic curves [ROC AUC] > 0.7) in at least 1 trial. Furthermore, univariate analysis showed a significant association between these antibody measures and protection (odds ratios 2.6-3.1). Multivariate modeling of combined data from 3 RTS,S CHMI trials identified the combination of IgG1 NANP binding magnitude plus serum NANP and N-junction Ig binding magnitude-avidity composite as the best predictor of protection (95% confidence interval for ROC AUC 0.693-0.834).CONCLUSIONThese results reinforce our previous findings and provide a tool for predicting protection in future trials.TRIAL REGISTRATIONClinicalTrials.gov NCT03162614, NCT03824236, NCT01366534, and NCT01857869.FUNDINGThis study was supported by Bill & Melinda Gates Foundation's Global Health-Discovery Collaboratory grants (INV-008612 and INV-043419) to GDT.

Nomogram to Predict Nodal Recurrence-Free Survival in Early Oral Squamous Cell Carcinoma.

This study aimed to develop and internally validate a prognostic nomogram for predicting nodal recurrence-free survival (NRFS) in patients with early-stage oral squamous cell carcinoma (OSCC) with clinically negative neck lymph nodes. The management of early-stage oral cancer patients with clinically negative neck lymph nodes (cN0) remains controversial, especially concerning the need for elective neck dissection. Data from a single institution spanning 2010 to 2020 were utilized to develop and evaluate the nomogram. The nomogram was constructed using multivariable Cox regression and LASSO regression analyses to identify independent risk factors for lymph node metastasis. Internal validation was performed using bootstrap resampling to assess the nomogram's predictive accuracy. A total of 930 cN0 patients with T1 and T2 stage OSCC were randomly divided into training and validation cohorts (8:2 ratio). Independent risk factors for lymph node metastasis included tumor pathological grade (well: reference, moderate/poor: OR 1.69), cT (cT1: reference, cT2: OR 2.01), history of drinking (never: reference, current/former: OR 1.72), and depth of invasion (0 mm < DOI ≤ 5 mm: reference, 5 mm < DOI ≤ 10 mm: OR 1.31). The nomogram, incorporating these variables, demonstrated good predictive accuracy with a C-index of 0.67 (95% CI: 0.58-0.76) in the validation set. In both training and validation groups, the nomogram effectively stratified patients into low-risk and high-risk groups for occult cervical nodal metastases (p < 0.05). The nomogram enables risk stratification and improved identification of occult cervical nodal metastases in clinically node-negative OSCC patients by incorporating tumor-specific and patient-specific risk factors.

Regional vehicle energy consumption evaluation framework to quantify the benefits of vehicle electrification in plateau city: A case study of Xining, China

Vehicle electrification holds significant potential for driving both decarbonization and improved energy efficiency within the realm of road transportation. However, accurate quantification of energy consumption advantages offered by electric vehicles (EVs) has remained elusive, thus impeding their long-term development due to variations across cities and vehicle types. To address this challenge, we propose a novel regional vehicle energy consumption assessment framework that integrates a region-specific driving cycle (DC) database and advanced high-precision energy consumption models. The regional DC database is constructed using a Markov chain approach. The database is stochastic and diverse, while taking into account real driving features and regional geographic environments. Energy consumption models for internal combustion engine vehicle (ICEV), plug-in hybrid electric vehicle (PHEV) and battery electric vehicle (BEV) are constructed based on machine learning approach. These models have good generalization ability and prediction accuracy, with R2 of 0.86, 0.86 and 0.77 on the test set, respectively. According to the interpretability of the models, vehicle acceleration and vehicle specific power are considered to be the most important features affecting vehicle energy consumption. According to the evaluation results, compared to using ICEV, the promotion of PHEV and BEV in Xining, a plateau city, can reduce energy consumption by 27.83 % and 32.05 %, respectively. The framework not only unifies the scale of energy consumption evaluation among different vehicle types, but also its good generalization ability can be migrated and extended to the evaluation of vehicle energy consumption in various regions. The establishment of this framework will contribute to the promotion and popularization of electric vehicles, as well as the advancement of energy-saving initiatives in the transportation sector.

Virtual validation of a Metro rolling-stock for exterior and interior noise in standard controlled conditions

Validation of rolling-stock is a time and cost expensive task during the development of train. Replacing type tests by simulation (virtual validation) that fits the need of the complete railway industry in terms of accuracy and uncertainty, is possible in some cases. Simulation is widely used by train manufacturers during development of new products with good accuracy of prediction in a controlled environment as free field and ballasted track. Methodologies available are able to predict correctly in controlled conditions (ISO 3095/3381) interior and exterior noise for different operating conditions. In this paper, simulation cases will be presented for a standard metro product operating in controlled conditions. Simulations will be compared with experimental data for interior and exterior noise. Exterior noise modelling and propagation is done with analytical tools while interior noise is modeled using an experimental-numerical (beam-tracing) approach. Input data for simulations corresponds to numerical models of rolling noise and measured sound power level of equipment. Validation of the models are discussed and analyzed to confirm the reliability of virtual validation for rolling stock operating at controlled conditions. A proposal of a virtual validation approach based on uncertainties of the simulation is also discussed.