Greater Predictive Accuracy Research Articles

An improved prediction of high-performance concrete compressive strength using ensemble models and neural networks

Traditional methods for proportioning of high-performance concrete (HPC) have certain shortcomings, such as high costs, usage constraints, and nonlinear relationships. Implementing a strategy to optimize the mixtures of HPC can minimize design expenses, time spent, and material wastage in the construction sector. Due to HPC's exceptional qualities, such as high strength (HS), fluidity and resilience, it has been broadly used in construction projects. In this study, we employed Generalized Regression Neural Network (GRNN), Nonlinear AutoRegressive with exogenous inputs (NARX neural network), and Random Forest (RF) models to estimate the Compressive Strength (CS) of HPC in the first scenario. In contrast, the second scenario involved the development of an ensemble model using the Radial Basis Function Neural Network (RBFNN) to detect inferior performance of standalone model combinations. The output variable was the 28 Days CS in MPa, while the input variables included slump (S), water-binder ratio (W/B) %, water content (W) kg/m3, fine aggregate ratio (S/a) %, silica fume (SF)%, and superplasticizer (SP) kg/m3. An RF model was developed by using R Studio; GRNN and NARX-NN models were developed by using the MATLAB 2019a toolkit; and the pre- and post-processing of data was carried out by using E-Views 12.0. The results indicate that in the first scenario, the Combination M1 of the RF model outperformed other models, with greater prediction accuracy, yielding a PCC of 0.854 and MAPE of 4.349 during the calibration phase. In the second scenario, the ensemble of RF models surpassed all other models, achieving a PCC of 0.961 and MAPE of 0.952 during the calibration phase. Overall, the proposed models demonstrate significant value in predicting the CS of HPC.

MACHINE LEARNING ALGORITHMS FOR PREDICTION OF AMBULATION AND WHEELCHAIR TRANSFER ABILITY IN SPINA BIFIDA

To determine which statistical techniques enhance our ability to predict ambulation and transfer ability in people with spina bifida (SB). Retrospective cohort study SETTING: 35 United States outpatient SB clinic sites PARTICIPANTS: individuals (n=4,589) with SB ages 5-73 (median age =13.59) INTERVENTION: not applicable MAIN OUTCOME MEASURE: ambulation ability, which consisted of the following categories: community ambulators, household ambulators, therapeutic ambulators, and non-ambulators SECONDARY OUTCOME: wheelchair transfer ability, as defined by the ability to transfer in and out of a wheelchair unassisted RESULTS: A Recurrent Neural Network (RNN) utilizing a multilayer perceptron discarded 76 cases during case processing, resulting in 4513 that were run through the RNN. The predictions in the resulting testing dataset were 83.22% accurate. Recall was 93.21% for community ambulators, 10.00% for household ambulators, 23.96% for therapeutic ambulators, and 76.70% for non-ambulators. Precision was 85.34% for community ambulators, 16.05% for household ambulators,16.67% for therapeutic ambulators, and 93.47% for non-ambulators. Total predictions included 68.39% for community ambulators, 2.25% for household ambulators, 3.83% for therapeutic ambulators, and 25.53% for non-ambulators. Correspondingly, the model accurately classified 70% of wheelchair transfers, while correctly identifying 97.3% of those able to transfer unassisted. RNN models hold promise for prediction of functional outcomes such as ambulation and transfer ability in people with SB, particularly for community and non-ambulators. Compared to previous work utilizing traditional logistic regression approaches which misclassified 16% of cases, the RNN resulted in greater prediction accuracy with fewer than 7% of cases misclassified.

Prediction model of permeability index for blast furnace based on WD-NL-transformer

The permeability index is an important variable to reflect the operating state of a blast furnace quickly, intuitively and comprehensively. Given the multi-scale, nonlinear and massive characteristics of the blast furnace smelting process data, a prediction model of permeability index based on the WD-NL-transformer (wavelet de-noising-nonlinear-transformer) method was proposed. The key variables affecting permeability index were selected from multiple perspectives by Pearson correlation coefficient and copula entropy. The extreme outliers were optimised using box plots and Lagrange linear interpolation. A data set was constructed after wavelet de-noising to build the prediction model with nonlinear processing capability, time-series prediction capability, and early stopping method. The results show that the MSE of WD-NL-transformer is 0.0093, with the RMSE of 0.0086. Meanwhile, the R2 is 0.9859, and the running time is 19.59 s, which means the model has great prediction accuracy and inference speed. The proposed model could provide theoretical support for advanced control of permeability index, which is of practical significance to ensure the stability of blast furnace smelting and accelerate the metallurgical process of intelligent automation.

Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes

This paper proposes development of optimized heterogeneous ensemble models for prediction of responses based on given sets of input parameters for wire electrical discharge machining (WEDM) processes, which have found immense applications in many of the present-day manufacturing industries because of their ability to generate complicated 2D and 3D profiles on hard-to-machine engineering materials. These ensembles are developed combining predictions of the three base models, i.e. random forest, support vector machine and ridge regression. These three base models are first framed utilizing the training datasets, providing predictions for all the responses under consideration. Based on these predictions, two optimization problems are formulated for each of the responses, while minimizing root mean squared error and mean absolute error, for subsequent development of two optimized ensembles whose predictions are the weighted sum of the predictions of the base models. The prediction performance of all the five models is ascertained through nine statistical metrics, after which a cumulative quality loss-based multi-response signal-to-noise (MRSN) ratio for each model is computed, for each of the responses, where a higher MRSN ratio indicates greater accuracy in prediction. This study is conducted using two experimental datasets of WEDM process. Overall, the optimized ensemble models having higher MRSN ratios than the base models are indicated to deliver better prediction accuracy.

Development and Evaluation of Ontogeny Functions of the Major UDP-Glucuronosyltransferase Enzymes to Underwrite Physiologically Based Pharmacokinetic Modeling in Pediatric Populations.

Uridine 5'-diphospho-glucuronosyltransferases (UGTs) demonstrate variable expression in the pediatric population. Thus, understanding of age-dependent maturation of UGTs is critical for accurate pediatric pharmacokinetics (PK) prediction of drugs that are susceptible for glucuronidation. Ontogeny functions of major UGTs have been previously developed and reported. However, those ontogeny functions are based on in vitro data (i.e., enzyme abundance, in vitro substrate activity, and so on) and therefore, may not translate to in vivo maturation of UGTs in the clinical setting. This report describes meta-analysis of the literature to develop and compare ontogeny functions for 8 primary UGTs (UGT1A1, UGT1A4, UGT1A6, UGT1A9, UGT2B7, UGT2B10, UGT2B15, and UGT2B17) based on published in vitro and in vivo studies. Once integrated with physiologically based pharmacokinetics modeling models, in vivo activity-based ontogeny functions demonstrated somewhat greater prediction accuracy (mean squared error, MSE: 0.05) compared to in vitro activity (MSE: 0.104) and in vitro abundance-based ontogeny functions (MSE: 0.129).

Baseline CT radiomics features to predict pathological complete response of advanced esophageal squamous cell carcinoma treated with neoadjuvant chemotherapy using paclitaxel and cisplatin

PurposeTo develop a CT radiomics model to predict pathological complete response (pCR) of advanced esophageal squamous cell carcinoma (ESCC) toneoadjuvant chemotherapy using paclitaxel and cisplatin. Materials and Methods326 consecutive patients with advanced ESCC from two hospitals undergoing baseline contrast-enhanced CT followed by neoadjuvant chemotherapy using paclitaxel and cisplatin were enrolled, including 115 patients achieving pCR and 211 patients without pCR. Of the 271 cases from 1st hospital, 188 and 83 cases were randomly allocated to the training and test cohorts, respectively. The 55 patients from a second hospital were assigned as an external validation cohort. Region of interest was segmented on the baseline thoracic contrast-enhanced CT. Useful radiomics features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomics features were chosen using support vector machine (SVM). Discriminating performance was assessed with area under the receiver operating characteristic curve (ROC) and F-1score. The calibration curves and Brier score were used to evaluate the predictive accuracy. ResultsEight radiomics features were selected to create radiomics models related to pCR of advanced ESCC (P-values < 0.01 for both the training and test cohorts). SVM model showed the best performance (AUCs = 0.929, 0.868 and 0.866, F-1scores = 0.857, 0.847 and 0.737 in the training, test and external validation cohorts, respectively). The calibration curves and Brier scores indicated goodness-of-fit and its great predictive accuracy. ConclusionCT radiomics models could well help predict pCR of advanced ESCC, and SVM model could be a suitable predictive model.

Application of GIS-Based Multi-Criteria Decision Analysis of Hydro-Geomorphological Factors for Flash Flood Susceptibility Mapping in Bangladesh

Flash floods are one of the most prevalent natural disasters, triggering deadly damage to homesteads, crops, infrastructure, road networks, communications, and the natural environment in the Haor (Wetland) region of Bangladesh. The purpose of the study aims to identify eleven (11) hydro-geomorphological driving factors, namely elevation, slope, aspect, rainfall, land use and land cover (LULC), lithology, soil type, topographic wetness index (TWI), NDVI, distance from the river, and drainage density, which are being explored for mapping flood-prone areas. This research has produced a flash flood susceptibility map using the Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP), which are interactive decision-making approaches under multi-criteria decision analysis (MCDA) in ArcGIS 10.8. The findings of this study showed that the susceptibility to flood hazards differs significantly among the seven Haor districts. As a result of the ANP and AHP, a more significant proportion of the Haor region is moderately susceptible to flooding (8685.09 to 9275.15 sq. km.), whereas 35.34% to 38.32% (7069.70 to 7668.67 sq. km.) accounts for high susceptible to flooding. Furthermore, 200 flood locations were identified in the northeast Haor region, where 140 (70%) randomly selected floods were used for training, and the remaining 60 (30%) were employed for validation purposes. The validation results showed that the AHP model had greater prediction accuracy (AUROC = 92.1%) than the ANP (AUROC = 88.5%) model. Therefore, the study findings can be helpful for researchers, academics, policymakers, and planners for sustainable flood mitigation strategies, particularly in Haor areas.

Bayesian risk prediction model: An accessible strategy to predict cadmium contamination risk in wheat grain grown in alkaline soils

Excessive cadmium (Cd) concentration in wheat grain is becoming a widespread concern in China. Considering the complexity of Cd transfer in the soil-wheat system, how the Cd risk in wheat grain be accurately predicted from the limited details available is of great significance for the risk management of Cd. Bayes' theory could leverage existing data by combining prior information and observational data, providing a promising strategy with which to calculate a more robust posterior probability of a grain sample exceeding the food safety standard (FSS) for Cd (0.1 mg kg−1). In the current study, a risk prediction model, based on Bayes’ theory, was established to achieve a more accurate prediction of the wheat grain Cd risk from a limited number of soil parameters. The risk prediction model could predict the risk probability of wheat grain with a Cd concentration exceeding the FSS under a given soil concentration of either total Cd or diethylenetriaminepentaacetic acid (DTPA)-extractable Cd. Soil total Cd concentration proved to be a better variable for the model with greater predictive accuracy. The model predicted that fewer than 5% of the wheat grain would have a Cd concentration exceeding the FSS when grown in soil with a total Cd concentration of less than 0.299 mg kg−1. The risk probability rose significantly to 50% when the soil total Cd reached 0.778 mg kg−1. The accuracy of the model was greater than the widely applied multiple linear regression model, whereas previously published data from similar soil conditions also confirmed that the Bayesian model could predict wheat Cd risk with minimal error. The proposed model provides an accurate, accessible and cost-effective methodology for predicting Cd risk in wheat grown in alkaline soils before harvest. The wider application to other soil conditions, crops or contaminants using the Bayesian model is also promising for risk management authorities.

Concrete Breaking Strength Prediction Using Machine Learning

Concrete Breaking Strength Prediction Using Machine Learning

Attention mechanism-aided model ensemble method of chiller energy consumption prediction

The chiller energy consumption prediction plays an important role in reducing the building energy consumption. Continuously improving the performance of the prediction model is the key to ensuring the accuracy of the chiller energy consumption prediction. As a result, a new improved ensemble model of the chiller energy consumption prediction is proposed, based on the idea of algorithmic ensemble aided by an attention mechanism (AM). To guarantee that the proposed model can see information from many spatial and structural viewpoints, the improved ensemble model incorporates the benefits of several base prediction methods. The suggested approach chooses crucial features to simplify the model input by utilizing AM, thereby reducing the complexity of the samples. The improved ensemble model was experimentally verified in an actual multi-chiller system, and the results showed that the established model could obtain good prediction results. Finally, the proposed model was compared with existing, well-known prediction models. To evaluate the accuracy of these models, the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), variance accounted for (VAF), and coefficient of determination (R2) were used as the evaluation indices. With an MAE of 2.858, an MAPE of 1.2755 %, an MSE of 20.1878, a VAF of 93.6058, and an R2 of 0.9923, the findings demonstrate that the improved ensemble model has a greater prediction accuracy than the other well-known prediction methods. The suggested model enhances the variety of empirical model algorithm libraries.

Abstract PS10-07: A second-generation polygenic risk score (PRS) based on genetic ancestry improves breast cancer (BC) risk prediction for all ancestries

Abstract Background: Common genetic variants, mainly single-nucleotide polymorphisms (SNPs) explain substantial genetic susceptibility to BC. PRS have been developed to quantify the combined effects of BC-associated SNPs, providing important information about BC risk. Historically, genome-wide association studies have been conducted in predominantly European populations, resulting in miscalibrated and inaccurate PRS for non-Europeans. We previously described a multiple-ancestry PRS (MA-PRS-149) based on 56 ancestry-informative and 93 BC-associated SNPs. The MA-PRS-149 achieved accuracy for all women by characterizing the genetic ancestry of each BC-associated SNP in terms of three reference ancestries (African, East Asian, and European), applying ancestry-specific SNP risks and frequencies, and combining the results as a weighted sum of three ancestry-specific PRS. Here, we aimed to improve the predictive accuracy of MA-PRS-149, particularly for non-Europeans, through the inclusion of additional BC-associated SNPs. Methods: Women referred for hereditary cancer testing and negative for pathogenic variants in BC-associated genes were divided into consecutive study cohorts to (1) quantify ancestry-specific SNP risks, (2) combine the three ancestry-specific PRS, and (3) pre-specified clinical validation. To select an optimal set of BC-associated SNPs, we developed a novel synthetic stepwise regression methodology that accounts for linkage disequilibrium. Ancestry-specific SNP risks were determined from meta-analyses of literature with clinical cohorts of 57,827 Black/African and 26,992 East Asian women. Ancestry-specific PRS were combined based on a diverse cohort of 157,740 women. Clinical validation was conducted in an independent cohort of 77,774 women. We used multivariable logistic regression adjusted for age, ancestry, and cancer history to test for improved BC risk prediction over clinical factors. We tested for improvement over the MA-PRS-149, and a European PRS, by including additional PRS as covariates. Calibration was assessed through goodness-of-fit tests. All analyses were conducted within the full cohort and ancestral subpopulations. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported per standard deviation within the corresponding population. Results: An optimal set of 383 SNPs (56 ancestry-informative and 327 BC-associated) was included in the final PRS (MA-PRS-383). MA-PRS-383 added significant predictive information to clinical factors in the full cohort and within each ancestry (Table 1). MA-PRS-383 had greater predictive accuracy than MA-PRS-149 or a 383-SNP PRS with European weights. Goodness-of-fit tests showed that MA-PRS-383 was well-calibrated and predicted risk accurately in the tails of the distribution for both European and non-European women. Conclusion: MA-PRS-383 was well-calibrated and substantially improved upon existing PRS in all tested ancestral populations. Incorporation of MA-PRS-383 into BC risk assessment may lead to more accurate identification of women who are most likely to benefit from screening and preventive medications. Citation Format: Timothy Simmons, Elisha Hughes, Dmitry Pruss, Matthew Kucera, Benjamin Roa, Thaddeus Judkins, Thomas Slavin, Victor Abkevich, Ryan Hoff, Srikanth Jammulapati, Susanne Wagner, Dale Muzzey, Jerry Lanchbury, Alexander Gutin. A second-generation polygenic risk score (PRS) based on genetic ancestry improves breast cancer (BC) risk prediction for all ancestries [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-07.

A novel intelligent risk prediction model for the effectiveness of CO2/N2–ECGD technology

AbstractTo solve the problems of high sampling requirements and low predictive accuracy resulting from the complexity, uniqueness, and randomness of predicting the risk of the CO2 and N2 injection to enhance coal seam gas drainage (CO2/N2–ECGD) technology. The principal component analysis (PCA) method to reduce the dimensionality of the factor data that contribute to the effect risk of the technology was adopted. And the particle swarm optimization (PSO) method was implemented to search for optimal hyperparameters in support vector machine (SVM) by particle search, as a solution to the traditional SVM hyperparameters optimization problem. A novel risk prediction model using machine learning algorithms for gas injection displacement technology was constructed. The prediction results were tested and compared with those of backpropagation (BP), Random Forest (RF), and Decision Tree (DT) models using data from 29 gas injection displacement field projects in China. The results demonstrated that the SVM model had greater accuracy in prediction than the other three models. Additionally, after PSO optimization and dimensionality reduction, the PCA–PSO–SVM model reached 100% prediction accuracy, while requiring less modeling and operation time. The study provided a reliable and reasonable model for predicting technical effects, along with a theoretical basis for risk management and prevention. First, the technology's influencing indicators were analyzed by examining its mechanisms. Second, we utilized the PCA method to reduce the dimensionality of the factor data that contribute to the risk of the technology's effects. Third, we implemented the PSO method to search for optimal hyperparameters in the SVM through particle search, as a solution to the traditional SVM hyperparameters optimization problem. Finally, the prediction results were tested and compared with those of BP, RF, and DT models using data from 29 gas injection displacement field projects in China. The SVM model was found to have greater accuracy in prediction than the other three models. After PSO optimization and dimensionality reduction, the PCA–PSO–SVM model achieved 100% prediction accuracy while requiring less modeling and operation time. The study presents a valid and reasonable model for predicting technical effects and a theoretical basis for risk management and prevention.

A Comparative Study on the Results of College English Grade 4 Based on Multi-model Prediction

Predictive analytics improves educational outcomes by offering insights into student performance and influencing targeted interventions. In this work, we undertake a detailed comparative analysis of predictive models for projecting College English Grade 4 exam results, to contribute to the improvement of educational predictive analytics. We compare the predicted accuracy and interpretability of several modeling approaches, including ensemble learning methods like Random Forests, interpretable models like Decision Trees, and Support Vector Machines (SVM). We evaluate each model's performance using important metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2) coefficients. Our results show that Random Forests have greater prediction accuracy, beating other models in terms of MSE, RMSE, and MAE. Despite its slightly lower R2 coefficients, Decision Trees perform competitively and provide useful insights into crucial variables. In contrast, SVM shows limitations in prediction accuracy for College English Grade 4 exam results. We highlight the relevance of these findings for educators and administrators, emphasizing the need to make educated decisions when selecting predictive models and implementing targeted interventions to promote student achievement. This work advances the field of educational predictive analytics by giving empirical evidence for the efficacy of various modeling methodologies and emphasizing the importance of model interpretability in understanding student performance determinants. More study is needed to improve predictive analytics methods and enhance their applicability in educational settings.

Analysis of remaining useful life of slope based on nonlinear wiener process

A remaining useful life (RUL) prediction model based on the nonlinear Wiener process is proposed to better tackle the life evaluation problem in the slope degradation process. Taking the displacement of the slope as its performance degradation index, and the nonlinear Wiener process is used to establish the RUL prediction model of the slope. For this model, the least squares method (LSM) is used to estimate the drift coefficients, the maximum likelihood estimation method (MLEM) is used to estimate the diffusion parameters, and then the probability density function (PDF) of the RUL of the slope is deduced and the RUL is predicted. The proposed model is verified by slope engineering examples. The results demonstrated that the RUL of the degradation model based on the nonlinear Wiener process has a greater prediction accuracy than the linear Wiener process. Because the various nonlinear functions have varying slope adaptations, and it can predict the RUL of a slope more accurately, which can provide more reliable preventive maintenance decisions.

Comparison of Predictive Performance of Data Mining Algorithms in Predicting Tomato Yield with the A Case Study in Igdir

Among the vegetable species in the world, the plant with the most cultivation area is tomato. Increasing tomato yield is important in terms of contributing more to the world economy, producer’s income and human health. With the advancement in software technologies, the importance of data mining algorithms is increasing due to the fact that these algorithms can produce more sophisticated solutions for regression and classification problems. Determining the factors affecting tomato yield and comparing different data mining algorithms on prediction of tomato yield are the purpose of this study. For this purpose, survey study was conducted with the 105 farmers, selected by Simple Random Sampling Method in Igdir province in 2016. Different data mining algorithms including Classification and Regression Tree, Exhaustive CHAID, Chi-Square Automatic Interaction Detector, Artificial Neural Network Algorithm, Multivariate Adaptive Regression Splines and General Linear Model were developed and compared their predictive performance. MARS decision tree has been able to build a model with greatest predictive accuracy, and the others are respectively ANN, GLM, CART, CHAID and Exhaustive CHAID. In the MARS model, number of irrigation , amount of chemical fertilizer , age of farmer , number of seedlings , education level , soil analysis status , sowing region were found statistically significant (P˂0.05). Preferring the MARS model could give an opportunity to detect factors affecting tomato yield and their interactions with higher accuracy. Moreover, results can be easily interpreted and the rules are understandable.

The Value of Computed Tomography Scan in Three-dimensional Planning and Intraoperative Navigation in Primary Total Hip Arthroplasty.

Total hip arthroplasty (THA) is a frequently performed procedure; the objective is restoration of native hip biomechanics and achieving functional range of motion (ROM) through precise positioning of the prosthetic components. Advanced three-dimensional (3D) imaging and computed tomography (CT)-based navigation are valuable tools in both the preoperative planning and intraoperative execution. The aim of this study is to provide a thorough overview on the applications of CT scans in both the preoperative and intraoperative settings of primary THA. Preoperative planning using CT-based 3D imaging enables greater accuracy in prediction of implant sizes, leading to enhancement of surgical workflow with optimization of implant inventory. Surgeons can perform a more thorough assessment of posterior and anterior acetabular wall coverage, acetabular osteophytes, anatomical landmarks, and thus achieve more functional implant positioning. Intraoperative CT-based navigation can facilitate precise execution of the preoperative plan, to attain optimal positioning of the prosthetic components to avoid impingement. Medial reaming can be minimized preserving native bone stock, which can enable restoration of femoral, acetabular, and combined offsets. In addition, it is associated with greater accuracy in leg length adjustment, a critical factor in patients' postoperative satisfaction. Despite the higher costs and radiation exposure, which currently limits its widespread adoption, it offers many benefits, and the increasing interest in robotic surgery has facilitated its integration into routine practice. Conducting additional research on ultra-low-dose CT scans and examining the potential for translation of 3D imaging into improved clinical outcomes will be necessary to warrant its expanded application.

Quantitatively Interpreting Residents Happiness Prediction by Considering Factor–Factor Interactions

Exploring the high-effect factors of residents’ happiness is good for a wide range of policy-making for economics and politics in most countries. Limited to the concerns of sociologists, previous efforts manually predefined the relationship among multifactors to analyze the factor–factor interactions with high interpretability by regression models. Recently, deep learning methods show great promising prediction accuracy by automatically learning additive interaction between factors, while it meets the challenges of interpretability. To this end, an unbiased post-hoc and model-agnostic method is promising to quantitatively interpret the results of the happiness prediction model. Thus, this article proposes a novel solution based on the deep neural network (DNN) and the Shapley value to compute the factor–factor interactions in different coalitions based on coalitional game theory. Aiming at evaluating the pairwise interactions interpretability quality of our solution, experiments are conducted on the Chinese General Social Survey (CGSS) and European Social Survey (ESS) questionnaire datasets. By systematic reviews, the experimental results are highly consistent with academic studies in social science. Analyzed by different factor categories, the new finding is that some factors, e.g., body mass index (BMI) and social media, play a crucial role in happiness prediction but are rarely considered in social science studies. Specifically, there are some heavy interactions across multicategories, such as personal information (health and age) with economics (insurance and income). Therefore, this solution can theoretically support the implications of social decision-making.

Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients: a retrospective cohort study

BackgroundSevere community-acquired pneumonia is one of the most lethal forms of CAP with high mortality. For rapid and accurate decisions, we developed a mortality prediction model specifically tailored for elderly SCAP patients.MethodsThe retrospective study included 2365 elderly patients. To construct and validate the nomogram, we randomly divided the patients into training and testing cohorts in a 70% versus 30% ratio. The primary outcome was in-hospital mortality. Univariate and multivariate logistic regression analyses were used in the training cohort to identify independent risk factors. The robustness of this model was assessed using the C index, ROC and AUC. DCA was employed to evaluate the predictive accuracy of the model.ResultsSix factors were used as independent risk factors for in-hospital mortality to construct the prediction model, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet, and BUN. The C index was 0.743 (95% CI 0.719–0.768) in the training cohort and 0.731 (95% CI 0.694–0.768) in the testing cohort. The ROC curves and AUC for the training cohort and testing cohort (AUC = 0.742 vs. 0.728) indicated a robust discrimination. And the calibration plots showed a consistency between the prediction model probabilities and observed probabilities. Then, the DCA demonstrated great clinical practicality.ConclusionsThe nomogram incorporated six risk factors, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet and BUN, which had great predictive accuracy and robustness, while also demonstrating clinical practicality at ICU admission.

Field infestation of the invasive fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae) on maize in Southern Ethiopia

The biggest obstacle to Ethiopia's maize production is the fall armyworm (FAW), Spodoptera frugiperda. Since its recent introduction, the pest has assumed a key pest status displacing most other stalk borer insects known to attack maize in the country. This study was carried out to assess the infestation of FAW on maize, identify associated natural enemies, and determined the geographic distribution of the pest in southern Ethiopia. Infestation by FAW was encountered in most surveyed areas. The earwig, Doru luteipes (Scudder) was frequently found preying on larvae of FAW while the larvae of Chrysoperla rufilabris (Burmeister) were observed feeding on FAW egg masses. Megaselia scalaris (Loew) and Palexorista zonata (Curran), two parasitoids of FAW, were recovered from FAW larvae. The entomopathogenic fungus, Beauveria bassiana, was isolated from the cadaver of FAW larvae. Modeling the distribution of FAW revealed that there are many suitable places in southern Ethiopia, with low-lying plain lands having the greatest potential for pest invasion. With AUC values above 0.9, the MaxEnt models demonstrated great prediction accuracy. Precipitation in the driest month (27.3%) and precipitation in the coldest quarter (25.4%), accounted for more than half of the variance in the pest's potential distribution. The findings of this study have contributed to a greater understanding of field infestation of FAW and associated natural enemies. The species distribution model result constituted a crucial point in providing the rationale for devising management strategy by taking into account a specified area susceptible to enhance invasion risk by FAW.

Sound Pressure Slopes of Turbulent and Cavitating Liquids, and a Method to Predict Such Levels

Abstract This paper presents an author-created, unique method called the ABC method to predict sound pressure of turbulent and cavitating liquids according to ISA Standard 75.17 and is measured 1 m from the outer wall of a downstream pipe, attached to the outlet of a valve [Baumann, 2023, “Method to Establish Sound and Acceleration Levels of High Pressure Reducing Valves,” ASME Open J. Eng. 2, p. 001000-1]. The purpose of this paper is to explain how such sound pressure levels are estimated. It is accomplished by adding the results of equations in sub-sectors A, B, and C, all in dBA. This gives the sound pressure of a given valve handling water and installed in a Schedule 40 steel pipe. Additional modifiers are shown to modify the ABC method results for other pipes or fluids. The method is open and entirely based on known fluid-mechanic and acoustic laws such as Newton’s and Lighthill’s. It is claimed that such methods will give prediction results superior to those expected using the current International Electrical Commission (IEC) Standard 60534-8-4 [Baumann, 1970, “On the Prediction of Aerodynamically Created Sound Pressure Levels of Control Valves,” ASME Paper WA/FE—28, Presented at the Annual ASME Winter Meeting, November]. A table is shown presenting test data which are compared to ABC method calculated values. Additional graphical information supporting such great prediction accuracy is also shown.