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Postoperative pneumonia (POP) is a common complication after hip fracture surgery and is associated with increased mortality and other complications in elderly patients. This study aims to evaluate biomarkers, especially the glucose-albumin ratio (GAR), for predicting POP in elderly hip fracture patients. A total of 1279 elderly patients admitted to our hospital with hip fractures were included. We assessed 29 biomarkers and focused on GAR to determine its prognostic and predictive value for POP. Multivariable logistic regression and propensity score-matched analyses were conducted to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for POP, adjusting for potential confounders. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off of GAR for predicting POP. Among the biomarkers and combinations assessed, GAR demonstrated superior predictive capability for POP in elderly hip fracture patients. ROC analyses showed moderate predictive accuracy of GAR for POP, with an area under the curve of 0.750. Using the optimal cut-off of 0.175, the high GAR group was significantly associated with increased odds of POP (adjusted OR 2.14, 95%, CI 1.50–3.05). These associations remained significant after propensity score matching and subgroup analyses. Dose–response relationships between GAR and POP were observed. In conclusion, GAR may be a promising biomarker to predict POP risk in elderly hip fracture patients. Further studies are warranted to validate its clinical utility. However, this study has certain limitations, including its retrospective design, potential for selection bias due to the exclusion criteria, and the single-center nature of the study, which should be addressed in future prospective, multicenter studies. [ABSTRACT FROM AUTHOR]

Forecasting plays a vital role in solar power generation and skillfully managing renewable energy resources. The traditional artificial neural network (ANN) has certain limitations with its pre-defined network structure in predicting solar power potential and not suitable because traditional ANN must be configured. Configuring the ANN model is time-consuming through manual process or trial and error. Therefore, this work evaluates the combination of different optimization techniques for ANN model configuration. In other techniques, the forecast performance is between 84.9 and 91.6% on average. Experience in handling the traditional methods helps develop a novel evolution of cub to predator (ECP) optimization technique. The investigation revealed that incorporating optimization results in superior performance over the traditional ANN, whereas the proposed ECP unveils 97.2%, which is an excellent result of contest optimization techniques. At the same time, the proposed model with the lowest MSE = 0.0029, and MRE = 0.0809. [ABSTRACT FROM AUTHOR]

Due to the abrupt rise in the number of vehicles on the road, there is an increasing risk of traffic accidents, which can result in fatalities and damage to economic resources. This is due to the rapid growth in the human population and the development of motorization. The biggest issuewith anticipating and analyzing data on road accidents is the small quantity of the dataset that may be used for study in this area. Road accidents have a low geographical and temporal density, despite the fact that they cause millions of deaths and injuries annually. The purpose of this article is to suggest a strategy for improving methodologies for determining how frequently traffic accidents occur in Poland. Techniques for multi-criteria optimization were used in this. We can infer from the findings that the suggested method can be utilized to determine the best methods for forecasting the frequency of traffic accidents. The method can successfully be used to forecast other events not only logistics or transportation. [ABSTRACT FROM AUTHOR]

In the ever-evolving media landscape, television (TV) remains a coveted platform, compelling industry players to innovate amid intense competition. This study focuses on leveraging machine learning regression models to precisely predict TV program reach. Our objective is to assess the models' efficacy, revealing a standout performer with a mean absolute percent error of just under 8%. Significantly, we identify features exerting a substantial impact on predictions and explore the potential for model enhancement through expanded datasets. This research extends beyond statistical insights, offering actionable implications for TV channel managers. Empowered by these findings, managers can make informed decisions in program planning and scheduling, optimizing viewer engagement. The temporal analysis of evolving trends over time adds a nuanced layer to our study, aligning it with the dynamic nature of the media landscape. As television retains its dynamic force, our insights contribute not only to academic discourse but also provide practical guidance, enhancing the competitive edge of television channels. [ABSTRACT FROM AUTHOR]

Backgroud: Intussusception is a common acute abdominal disease in children, often leading to acute ileus in infants and young children. Objective: This study aimed to develop and validate a nomogram for predicting recurrent intussusception in children within 48 h after pneumatic reduction of primary intussusception. Methods: Clinical data of children with acute intussusception admitted to multiple hospitals from March 2019 to March 2021 were retrospectively analyzed. The children were divided into a successful reductioncontrol group (control group) and a recurrent intussusception group (RI group) according to the results of pneumatic reduction. Results: A total of 2406 cases were included in this study, including 2198 control group and 208 RI group. In the total sample, 1684 cases were trained and 722 cases were verified. A logistic regression analysis was conducted to establish a predictive model based on age, abdominal pain time, white blood cells count, and hypersensitive C-reactive protein levels as independent predictors of intussusception recurrence. The nomogram successfully predicted recurrent intussusception after pneumatic reduction. Conclusion: In this study, a nomogram was developed based on clinical risk factors to predict recurrent intussusception following pneumatic reduction in children. Age, abdominal pain time, white blood cell counts, and hypersensitive C-reactive protein levels were identified as predictors and incorporated into the nomogram. Internal validation demonstrated that this nomogram can offer a clear and convenient tool for identifying risk factors for recurrence of intussusception in children undergoing pneumatic reduction. [ABSTRACT FROM AUTHOR]

Background: It is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index, an indicator of how large of a keyword is in Baidu's search volume, in the early warning and predicting the epidemic trend of COVID-19. Methods: The daily number of cases and the Baidu Search Index of 8 keywords (weighted by population) from December 1, 2019 to March 15, 2020 were collected and analyzed with times series and Spearman correlation with different time lag. To predict the daily number of COVID-19 cases using the Baidu Search Index, Zero-inflated negative binomial regression was used in phase 1 and negative binomial regression model was used in phase 2 and phase 3 based on the characteristic of independent variable. Results: The Baidu Search Index of all keywords in Wuhan was significantly higher than Hubei (excluded Wuhan) and China (excluded Hubei). Before the causative pathogen was identified, the search volume of "Influenza" and "Pneumonia" in Wuhan increased with the number of new onset cases, their correlation coefficient was 0.69 and 0.59, respectively. After the pathogen was public but before COVID-19 was classified as a notifiable disease, the search volume of "SARS", "Pneumonia", "Coronavirus" in all study areas increased with the number of new onset cases with the correlation coefficient was 0.69 ~ 0.89, while "Influenza" changed to negative correlated (rs: -0.56 ~ -0.64). After COVID-19 was closely monitored, the Baidu Search Index of "COVID-19", "Pneumonia", "Coronavirus", "SARS" and "Mask" could predict the epidemic trend with 15 days, 5 days and 6 days lead time, respectively in Wuhan, Hubei (excluded Wuhan) and China (excluded Hubei). The predicted number of cases would increase 1.84 and 4.81 folds, respectively than the actual number of cases in Wuhan and Hubei (excluded Wuhan) from 21 January to 9 February. Conclusion: The Baidu Search Index could be used in the early warning and predicting the epidemic trend of COVID-19, but the search keywords changed in different period. Considering the time lag from onset to diagnosis, especially in the areas with medical resources shortage, internet search data can be a highly effective supplement of the existing surveillance system. [ABSTRACT FROM AUTHOR]

Background: Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related nonpharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. Methods: The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA--GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. Results: Considering the MAPE, RMSE, and R squared values, the ARMA--GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. Conclusions: Our study suggested that the ARMA--GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA--GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts. [ABSTRACT FROM AUTHOR]

Abstract Postoperative pneumonia (POP) is a common complication after hip fracture surgery and is associated with increased mortality and other complications in elderly patients. This study aims to evaluate biomarkers, especially the glucose-albumin ratio (GAR), for predicting POP in elderly hip fracture patients. A total of 1279 elderly patients admitted to our hospital with hip fractures were included. We assessed 29 biomarkers and focused on GAR to determine its prognostic and predictive value for POP. Multivariable logistic regression and propensity score-matched analyses were conducted to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for POP, adjusting for potential confounders. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off of GAR for predicting POP. Among the biomarkers and combinations assessed, GAR demonstrated superior predictive capability for POP in elderly hip fracture patients. ROC analyses showed moderate predictive accuracy of GAR for POP, with an area under the curve of 0.750. Using the optimal cut-off of 0.175, the high GAR group was significantly associated with increased odds of POP (adjusted OR 2.14, 95%, CI 1.50–3.05). These associations remained significant after propensity score matching and subgroup analyses. Dose–response relationships between GAR and POP were observed. In conclusion, GAR may be a promising biomarker to predict POP risk in elderly hip fracture patients. Further studies are warranted to validate its clinical utility. However, this study has certain limitations, including its retrospective design, potential for selection bias due to the exclusion criteria, and the single-center nature of the study, which should be addressed in future prospective, multicenter studies.

Abstract Backgroud Intussusception is a common acute abdominal disease in children, often leading to acute ileus in infants and young children. Objective This study aimed to develop and validate a nomogram for predicting recurrent intussusception in children within 48 h after pneumatic reduction of primary intussusception. Methods Clinical data of children with acute intussusception admitted to multiple hospitals from March 2019 to March 2021 were retrospectively analyzed. The children were divided into a successful reductioncontrol group (control group) and a recurrent intussusception group (RI group) according to the results of pneumatic reduction. Results A total of 2406 cases were included in this study, including 2198 control group and 208 RI group. In the total sample, 1684 cases were trained and 722 cases were verified. A logistic regression analysis was conducted to establish a predictive model based on age, abdominal pain time, white blood cells count, and hypersensitive C-reactive protein levels as independent predictors of intussusception recurrence. The nomogram successfully predicted recurrent intussusception after pneumatic reduction. Conclusion In this study, a nomogram was developed based on clinical risk factors to predict recurrent intussusception following pneumatic reduction in children. Age, abdominal pain time, white blood cell counts, and hypersensitive C-reactive protein levels were identified as predictors and incorporated into the nomogram. Internal validation demonstrated that this nomogram can offer a clear and convenient tool for identifying risk factors for recurrence of intussusception in children undergoing pneumatic reduction.

Abstract Background It is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index, an indicator of how large of a keyword is in Baidu’s search volume, in the early warning and predicting the epidemic trend of COVID-19. Methods The daily number of cases and the Baidu Search Index of 8 keywords (weighted by population) from December 1, 2019 to March 15, 2020 were collected and analyzed with times series and Spearman correlation with different time lag. To predict the daily number of COVID-19 cases using the Baidu Search Index, Zero-inflated negative binomial regression was used in phase 1 and negative binomial regression model was used in phase 2 and phase 3 based on the characteristic of independent variable. Results The Baidu Search Index of all keywords in Wuhan was significantly higher than Hubei (excluded Wuhan) and China (excluded Hubei). Before the causative pathogen was identified, the search volume of “Influenza” and “Pneumonia” in Wuhan increased with the number of new onset cases, their correlation coefficient was 0.69 and 0.59, respectively. After the pathogen was public but before COVID-19 was classified as a notifiable disease, the search volume of “SARS”, “Pneumonia”, “Coronavirus” in all study areas increased with the number of new onset cases with the correlation coefficient was 0.69 ~ 0.89, while “Influenza” changed to negative correlated (rs: -0.56 ~ -0.64). After COVID-19 was closely monitored, the Baidu Search Index of “COVID-19”, “Pneumonia”, “Coronavirus”, “SARS” and “Mask” could predict the epidemic trend with 15 days, 5 days and 6 days lead time, respectively in Wuhan, Hubei (excluded Wuhan) and China (excluded Hubei). The predicted number of cases would increase 1.84 and 4.81 folds, respectively than the actual number of cases in Wuhan and Hubei (excluded Wuhan) from 21 January to 9 February. Conclusion The Baidu Search Index could be used in the early warning and predicting the epidemic trend of COVID-19, but the search keywords changed in different period. Considering the time lag from onset to diagnosis, especially in the areas with medical resources shortage, internet search data can be a highly effective supplement of the existing surveillance system.

Controlling is considered as a versatile modern focus of management. It is widespread in various fields of human activity but has not found direct application in civil aviation yet. Meanwhile, any aviation organization is subject to the general laws of management, therefore, controlling can and should find its application in the management of an aviation organization. Due to the particular importance of safety management considerations, controlling, as a management concept that allows you to control processes rather than results, fits seamlessly into the procedures of safety management systems (SMS) of aviation service providers. In particular, the development and monitoring of safety performance indicators (SPI) can be considered as a key element of operational controlling. In the SMS, the procedure to deal with the SPI, in conjunction with the risks mitigation procedure for safety, is an essential component of the entire system. To ensure the effectiveness of this procedure in the Air Traffic Service (ATS) organization, it is necessary to develop a balanced overall SPI. As the analysis showed, the indicators applicable in ATS organizations are focused on considering only incidents of the same “weight” and do not objectively reflect a level of safety assurance at ATS and its dynamics. The article presents a variant of developing a revised balanced indicator, which considers less significant deviations from the proper ATS system operation, errors and violations of personnel. The indicator was developed on the basis of the expert survey of ATS specialists. Monitoring and predicting of indicators are also important tasks of operational controlling. These problems can be solved by various methods, the applicability and comparative effectiveness of some of them are discussed in this article. All calculations are based on real data of one of the major ATS organizations of the Russian Federation.

The availability of goods in a store is very important. Predicting is a tool that is used to help predict the data needed by an organization or company. The purpose of this study is to predict the sale of a product that has a high risk of damage and fast expiration time by using existing techniques in forecasting. Forecasting can also be used to make product stock safety at the XYZ Supermarket. The results of this study are in the form of forecasting the sale of a product in a store by using the existing methods of forecasting that are adjusted to the sales data of one product. The method used in forecasting is the ARIMA method, Trend Analysis, and Single Exponential Smoothing. Trend Analysis Method has the highest accuracy with MAPE 9.91%, which means that forecasting is very good, compared to ARIMA with MAPE 37.21% and Single Exponential Smoothing with MAPE 10%. So that the results of the Trend Analysis forecasting will be used for the decision-making process about forecasting stockpiles and stock safety in the future.

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study. [ABSTRACT FROM AUTHOR]

This study constructs a deep learning-based model to predict internet addiction among college students and analyzes significant influencing factors. A random survey of 4,895 students from a university in Shandong Province was conducted using questionnaires on general information, internet addiction (CIAS-R), personality (CBF-PI-B), psychological traits (SDS, SAS), parenting styles (EMBU), behavioral issues (SAS-C), and social support (ASSRS) to establish a database. A predictive model was developed using a 1D Convolutional Neural Network (1D-CNN), extracting key influencing factors of internet addiction. The model showed 92.77% accuracy, with high precision and recall rates for predicting normal users and addicts. The gradient calculation indicates that in second-year students, negative and withdrawal behaviors, depression, over-interfering families, and anxiety significantly contribute to Internet addiction, with factors exceeding 0.5. The 1D-CNN model offers robust performance and accuracy in predicting internet addiction, identifying significant factors for early prevention and potential integration with apps for real-time monitoring. [ABSTRACT FROM AUTHOR]

Antiplatelet drugs in patients increase the risk of intracranial hemorrhage (ICH), which can seriously affect patients' quality of life and even endanger their lives. Currently, there is no specific score for predicting the risk of ICH caused by antiplatelet drugs. We aimed to identify factors associated with ICH in patients on antiplatelet drugs and to construct and validate a predictive model that would provide a validated tool for the clinic. Data were obtained from the patient medical records inpatient system. Prediction models were built by logistic regression, the area under the curve (AUC), and column line plots. Internal validation, analytical identification and calibration of the model using AUC, calibration curves and Hosmer-Lemeshow test. The registration number of this study is ChiCTR2000031909, and the ethical review number is 2020KY087. This single-center retrospective study enrolled 753 patients treated with antiplatelet drugs, including 527 in the development cohort. Multifactorial analysis showed that male, headache or vomiting, hypertension, cerebrovascular disease, CT-defined white matter hypodensity, abnormal GCS, fibrinogen and D-dimer were independent risk factors for ICH, and lipid-lowering drugs was a protective factor. The model was constructed using these nine factors with an AUC value of 0.949. In the validation cohort, the model showed good discriminatory power with an AUC value of 0.943 and good calibration (Hosmer-Lemeshow test P value of 0.818). Based on 9 factors, we derived and validated a predictive model for ICH with antiplatelet drugs in patients. The model has good predictive value and may be an effective tool to reduce the occurrence of ICH. [ABSTRACT FROM AUTHOR]

Background: Recurrence can worsen conditions and increase mortality in ICH patients. Predicting the recurrence risk and preventing or treating these patients is a rational strategy to improve outcomes potentially. A machine learning model with improved performance is necessary to predict recurrence. Methods: We collected data from ICH patients in two hospitals for our retrospective training cohort and prospective testing cohort. The outcome was the recurrence within one year. We constructed logistic regression, support vector machine (SVM), decision trees, Voting Classifier, random forest, and XGBoost models for prediction. Results: The model included age, NIHSS score at discharge, hematoma volume at admission and discharge, PLT, AST, and CRP levels at admission, use of hypotensive drugs and history of stroke. In internal validation, logistic regression demonstrated an AUC of 0.89 and precision of 0.81, SVM showed an AUC of 0.93 and precision of 0.90, the random forest achieved an AUC of 0.95 and precision of 0.93, and XGBoost scored an AUC of 0.95 and precision of 0.92. In external validation, logistic regression achieved an AUC of 0.81 and precision of 0.79, SVM obtained an AUC of 0.87 and precision of 0.76, the random forest reached an AUC of 0.92 and precision of 0.86, and XGBoost recorded an AUC of 0.93 and precision of 0.91. Conclusion: The machine learning models performed better in predicting ICH recurrence than traditional statistical models. The XGBoost model demonstrated the best comprehensive performance for predicting ICH recurrence in the external testing cohort. [ABSTRACT FROM AUTHOR]

Background: Malaria is considered the most deadly and difficult parasitic disease in the world. This study aims to use Markov chains to predict the probability patterns of stability or change in malaria incidence and deaths. Methods: Markov chains were used to analyze the data on malaria incidence and deaths through the Windows Quantitative Systems for Business (WINQSB) program. Data was obtained from the Ministry of Health, Gazira State, Health Information Centre, Sudan. The data is a time series, from 2001 to 2021 per year, according to three cases of decrease, stability, and increase. A transitional matrix is built for the three cases. Results: The results revealed that the probability that malaria incidence and deaths will reach a stable state in one year and in the long run; the probability of transitioning to an increased state was 0.66 of malaria incidence; and the probability of moving to a decreased state was 0.52 of malaria deaths. Conclusion: The results show that the malaria incidence will increase and malaria deaths will decrease in the short and long run from 2022 to 2030 in Gazira State. It is necessary to reinforce means and resources for case management and to investigate the determinants of the situation. Thus, strategies are urgently needed to arrest the unacceptably high incidence and death rates. [ABSTRACT FROM AUTHOR]

Renal colic (RC) is one of the most frequent reasons for presentation to the emergency department (ED) and creates a high economic and medical burden. Management strategies for RC range from waiting for spontaneous passage to surgical intervention. However, factors determining spontaneous stone passage (SSP) are still poorly understood. Therefore, in this study, we aimed to investigate the role of the systemic immune-inflammatory index (SII) in predicting SSP. We retrospectively analyzed the data of 924 patients aged over 18 years, who were diagnosed with RC in our clinic between 1 January 2019, and 30 May 2022, and had ureteral stones of ≤ 10 mm. The patients were divided into two groups according to whether they had SSP. The clinical and laboratory characteristics of the patients in the ED were evaluated. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and SII (neutrophil count x platelet count/lymphocyte count) values of the patients were calculated. Stone size and location were also recorded. In the univariate analysis of SSP, a ureteral stone size of ≤ 5 mm (p < 0.001), distal ureteral location (p < 0.001), SII (p < 0.001), NLR (p < 0.001), and PLR (p = 0.036) were significantly correlated with SSP. ROC analysis showed that an SII level < 721.8 (Sensitivity %82.6, Specificity %74.7, p < 0.001) was an independent predictor of SSP. Our findings showed that a low SII level was associated with SSP and could be used as a predictive marker of SSP as a more valuable parameter than NLR. SII and NLR, together with other indicators, are inflammatory markers that can be used in the clinical decision-making process for ureteral stone treatment. [ABSTRACT FROM AUTHOR]

The density of energetic materials plays a vital role in determining their performance. A novel method is proposed for reliable prediction of the density of energetic metal–organic frameworks (EMOFs). The experimental data of density for 142 EMOFs, with different metal nodes and ligands, are used to construct and examine the model. This model is based on the molecular weight, elemental composition, and some of the specific structural parameters, which can be obtained from the chemical structures of EMOFs. The presented model is simply applicable for EMOFs with complex compositions and structures, without using complicated computer codes. The statistical parameters and cross-validation (CV) technique were used for evaluating the accuracy, precision, goodness of fit, and goodness of prediction of the new model. The values of statistical parameters, such as mean absolute deviation (MAD), maximum of errors, root-mean-square error (RMSE), and the coefficient of determination (R2), are 0.09, 0.33, 0.12, and 0.94, respectively. The absolute percent error (APE) values are less than 5% in 93 data points and greater than 10% only in 7 cases. Finally, coefficients of determination in CV, i.e., Q2LOO and Q25-fold, are 0.93 and 0.91, respectively. [ABSTRACT FROM AUTHOR]

Manual calculation of the compressive strength of concrete (CSC) is an expensive and time-consuming process. Soft computing methods outperform the statistical methods used to resolve these problems. Nonetheless, complicated prediction models are still incomplete and require more exploration. Artificial neural networks (ANNs) provide a better and faster technique featuring solitary hidden layers and have improved the generalization capacity. The present paper presents three ANN-based (shuffled complex evolution, evaporation rate based water cycle algorithm (ERWCA), and Cuckoo optimization algorithm) prediction models to anticipate the compressive strength of concrete efficiently. An available database from the UCI repository is employed to develop and access the model performance. A comparison is made between the prediction accuracies of the above three techniques. Using all models, a comparative investigation has been conducted to predict the compressive strength of concrete at the curing ages of 91, 56, and 28 days. The experimental findings obtained from the ERWCA-MLP method indicate its capability of robust CSC prediction. On average, this method achieves the minimum RMSE of 0.55314 and 0.43329 and R2 of 0.99803 and 0.99824. The statistical significance test and the comparative analysis of simulation results indicate the superiority of ERWCA-MLP in predicting the compressive strength of concrete. [ABSTRACT FROM AUTHOR]

Abstract Sepsis remains a critical concern in intensive care units due to its high mortality rate. Early identification and intervention are paramount to improving patient outcomes. In this study, we have proposed predictive models for early sepsis prediction based on time-series data, utilizing both CNN-Transformer and LSTM-Transformer architectures. By collecting time-series data from patients at 4, 8, and 12 h prior to sepsis diagnosis and subjecting it to various network models for analysis and comparison. In contrast to traditional recurrent neural networks, our model exhibited a substantial improvement of approximately 20%. On average, our model demonstrated an accuracy of 0.964 (± 0.018), a precision of 0.956 (± 0.012), a recall of 0.967 (± 0.012), and an F1 score of 0.959 (± 0.014). Furthermore, by adjusting the time window, it was observed that the Transformer-based model demonstrated exceptional predictive capabilities, particularly within the earlier time window (i.e., 12 h before onset), thus holding significant promise for early clinical diagnosis and intervention. Besides, we employed the SHAP algorithm to visualize the weight distribution of different features, enhancing the interpretability of our model and facilitating early clinical diagnosis and intervention.

This document is an editorial from the journal Frontiers in Immunology titled "Artificial intelligence in predicting, determining and controlling cell phenotype or tissue function in inflammatory diseases." The editorial discusses the use of artificial intelligence (AI) in addressing the complexities of inflammatory diseases and developing effective therapies. The editorial highlights seventeen selected manuscripts that explore various aspects of AI-driven analyses, including immune-related cell signatures, disease pathogenesis, and prediction of disease onset, progression, diagnosis, and therapy. The articles cover a wide range of inflammatory conditions and utilize machine learning and AI methods to identify immune modulation, molecular targets, and single cell phenotypes. The editorial concludes by emphasizing the importance of AI in advancing our understanding of inflammation and improving patient outcomes. [Extracted from the article]

Electro-optical detection systems face numerous challenges due to the complexity and difficulty of targeting controls for "low, slow and tiny" moving targets. In this paper, we present an optimal model of an advanced n-step adaptive Kalman filter and gyroscope short-term integration weighting fusion (nKF-Gyro) method with targeting control. A method is put forward to improve the model by adding a spherical coordinate system to design an adaptive Kalman filter to estimate target movements. The targeting error formation is analyzed in detail to reveal the relationship between tracking controller feedback and line-of-sight position correction. Based on the establishment of a targeting control coordinate system for tracking moving targets, a dual closed-loop composite optimization control model is proposed. The outer loop is used for estimating the motion parameters and predicting the future encounter point, while the inner loop is used for compensating the targeting error of various elements in the firing trajectory. Finally, the modeling method is substituted into the disturbance simulation verification, which can monitor and compensate for the targeting error of moving targets in real time. The results show that in the optimal model incorporating the nKF-Gyro method with targeting control, the error suppression was increased by up to 36.8% compared to that of traditional KF method and was 25% better than that of the traditional nKF method. [ABSTRACT FROM AUTHOR]

Concrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete (RAC) technologies in permafrost, it is essential to certify the stability in frost‐induced conditions. The main goal of this study was to use support vector regression (SVR) methods to forecast the frost durability (DF) of RAC on the basis of durability agent value in cold climates. Herein, three optimization methods called Ant lion optimization (ALO), Grey wolf optimization (GWO), and Henry Gas Solubility Optimization (HGSO) were employed for indicating optimal values of SVR key parameters. The results depicted that all developed models have capability in predicting the DF of RAC in cold regions. The values of OBJ as a comprehensive index depicted that the GWO−SVR model has the higher value at 0.0571 as the weakest model, then ALO−SVR at 0.0312 recognized as the second‐highest model, and finally the HGSO−SVR system at 0.0224 mentioned as outperformed model. ALO−SVR and GWO−SVR approaches were likewise capable of accurately forecasting the DF of RAC in cold regions, but the created HGSO−SVR method outperformed them all when taking into account the explanations and justifications. [ABSTRACT FROM AUTHOR]

Simple Summary: This research explores the transformative potential of artificial intelligence (AI) in the early detection of lung cancer. Through a comprehensive systematic review and meta-analysis, this study evaluates the effectiveness of AI models, emphasizing a promising avenue for improving diagnostic accuracy. Among 1024 identified records, 39 studies were meticulously selected and analyzed following the PRISMA guidelines. The findings highlight significant strides in AI's role, emphasizing the need for standardized protocols. Despite the observed heterogeneity, this study underscores AI's promising impact on lung cancer screening, laying the groundwork for future advancements in clinical practice. This research contributes crucial insights for healthcare professionals and researchers alike, aiming to enhance the early diagnosis and management of lung cancer. (1) Background: Lung cancer's high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI's performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI's role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI's potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI's accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI's potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future. [ABSTRACT FROM AUTHOR]

Sepsis remains a critical concern in intensive care units due to its high mortality rate. Early identification and intervention are paramount to improving patient outcomes. In this study, we have proposed predictive models for early sepsis prediction based on time-series data, utilizing both CNN-Transformer and LSTM-Transformer architectures. By collecting time-series data from patients at 4, 8, and 12 h prior to sepsis diagnosis and subjecting it to various network models for analysis and comparison. In contrast to traditional recurrent neural networks, our model exhibited a substantial improvement of approximately 20%. On average, our model demonstrated an accuracy of 0.964 (± 0.018), a precision of 0.956 (± 0.012), a recall of 0.967 (± 0.012), and an F1 score of 0.959 (± 0.014). Furthermore, by adjusting the time window, it was observed that the Transformer-based model demonstrated exceptional predictive capabilities, particularly within the earlier time window (i.e., 12 h before onset), thus holding significant promise for early clinical diagnosis and intervention. Besides, we employed the SHAP algorithm to visualize the weight distribution of different features, enhancing the interpretability of our model and facilitating early clinical diagnosis and intervention. [ABSTRACT FROM AUTHOR]

• AD onset and progression were accompanied with an unappreciated serum autoantibody response. • Seven candidate serum AD-specific autoantibodies were identified. • A combinatory signature of the seven autoantibodies showed high accuracy in distinguishing AD. • The autoantibody alternation provide an opportunity to identify patients with MCI. Autoimmunity plays a key role in the pathogenesis of Alzheimer's disease (AD). However, whether autoantibodies in peripheral blood can be used as biomarkers for AD has been elusive. Serum samples were obtained from 1,686 participants, including 767 with AD, 146 with mild cognitive impairment (MCI), 255 with other neurodegenerative diseases, and 518 healthy controls. Specific autoantibodies were measured using a custom-made immunoassay. Multivariate support vector machine models were employed to investigate the correlation between serum autoantibody levels and disease states. As a result, seven candidate AD-specific autoantibodies were identified, including MAPT, DNAJC8, KDM4D, SERF1A, CDKN1A, AGER, and ASXL1. A classification model with high accuracy (area under the curve (AUC) = 0.94) was established. Importantly, these autoantibodies could distinguish AD from other neurodegenerative diseases and out-performed amyloid and tau protein concentrations in cerebrospinal fluid in predicting cognitive decline (P < 0.001). This study indicated that AD onset and progression are possibly accompanied by an unappreciated serum autoantibody response. Therefore, future studies could optimize its application as a convenient biomarker for the early detection of AD. [ABSTRACT FROM AUTHOR]

Repositories of source code and their hosting platforms are important data sources for software project development and management processes. These sources allow for the extraction of historical data points for the product development process evaluation. Extracted data points reflect the previous development experience and allow future planning and active development tracking. The aim of this research is to create a predictive approach to control software development based on a time series extracted from repositories and hosting platforms. This article describes the method of extracting parameters from repositories, the approach to creating time series models and forecasting their behavior. Also, the article represents the proposed approach for software project analyses based on fuzzy logic principles. The novelty of this approach is the ability to perform an expert evaluation of different stages of software product development based on the forecasted values of interested parameters and a fuzzy rule base. [ABSTRACT FROM AUTHOR]

Introduction and Objective. Perinatal asphyxia (PA) is a condition of impaired blood gas exchange which, if persistent, leads to progressive hypoxemia and hypercapnia. PA is characterized by high neonatal mortality or neurodevelopmental disorders in the child's later life. PA is closely related to hypoxic-ischemic encephalopathy (HIE), which is a complication of perinatal hypoxia. In recent years, there has been a growing interest in the use of machine learning (ML) and artificial intelligence (AI) in medicine. The development of machine learning models can improve patient care by applying them to diagnosis, prognosis, decision support, and treatment recommendations. Review Methods. The data for the article was found using the Web of Science, PubMed, Scopus and Google Scholar websites which were thematically selected for work. Brief description of the state of knowledge. Currently, no sufficiently effective prophylactic and therapeutic methods can prevent HIE or death in newborns with PA. Hypothermia is currently the only available method that seems to improve the prognosis in neonates with PA; however, it is not completely effective. In recent years, research has been conducted in the use of machine learning models to predict the occurrence of neonatal asphyxia, based on the occurrence of specific risk factors. Additionally, research has been carried out on the use of neuroimaging in predicting the neurodevelopmental condition of children after neonatal asphyxia. Summary. Presently, there are no preventive and therapeutic methods that can prevent HIE or death in newborns with severe perinatal hypoxia. In addition to basic knowledge about asphyxia, the review presents issues that could become the beginning of future research in the use of machine learning in neonatology. [ABSTRACT FROM AUTHOR]

Objective: We aimed to identify risk factors in lymph node metastasis in early-stage non-small cell lung cancer (NSCLC) and predict lymph node metastasis. Methods: A total of 416 patients with clinical stage IA2-3 NSCLC who underwent lobectomy and lymph node dissection between July 2016 and December 2020 at National Cancer Center Hospital East were included. Multivariable logistic regression was performed to develop a model for predicting lymph node metastasis. Leave-one-out cross-validation was performed to evaluate the developing prediction model, and sensitivity, specificity, and concordance statistics were calculated to evaluate its diagnostic performance. Results: The formula for calculating the probability of pathological lymph node metastasis included SUVmax of the primary tumor and serum CEA level. The concordance statistics was 0.7452. When the cutoff value associated with the risk of incorrectly predicting pathological lymph node metastasis was 7.2%, the diagnostic sensitivity and specificity for predicting metastasis were 96.4% and 38.6%, respectively. Conclusions: We created a prediction model for lymph node metastasis in NSCLC by combining the SUVmax of the primary tumor and serum CEA levels, which showed a particularly strong association. This model is clinically useful as it successfully predicts negative lymph node metastasis in patients with clinical stage IA2-3 NSCLC. [ABSTRACT FROM AUTHOR]

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Background: The STarT MSK screening tool aims to categorise musculoskeletal patients into three risk groups for treatment stratification. The tool has been translated and validated into Hebrew. However, its ability to predict persistent disability in patients has yet to be evaluated. Objective: The primary aim of this study was to assess the ability of the Hebrew version of the STarT MSK tool to predict persistent disability in patients experiencing musculoskeletal pain. Methods: A prospective observational cohort study was conducted, recruiting 135 patients with musculoskeletal pain in five common areas: back, neck, shoulder, knee, or multisite pain over the age of 21. At the first consultation, all patients completed demographic information, the Focus On Therapeutic Outcomes (FOTO) questionnaire (function, pain, and fear avoidance score), and the STarT MSK questionnaire. The patients completed the FOTO questionnaire again at the end of the physiotherapy treatments. Results: 25 patients (18.5%) were classified into the low‐risk group, 68 patients (50.3%) into the medium‐risk group, and 42 (31.1%) into the high‐risk group. The baseline STarT MSK tool score demonstrated an excellent ability to identify patients at high risk of developing persistent disability (AUC = 0.795, 95% CI 0.716–0.873). Conclusions: The Hebrew version of the STarT MSK tool can differentiate between three chronic risk groups and has high predictive validity for chronicity. This may provide a tool to assist clinicians in identifying patients who require more intensive care, and thus, potentially prevent the transition to chronic disabling pain. [ABSTRACT FROM AUTHOR]

Purpose: Partial nephrectomy (PN) for large or complex renal tumors can be difficult and associated with a higher risk of recurrence than radical nephrectomy. We aim to evaluate the clinical useful of nephrometry scores for predicting oncological outcomes in a large cohort of patients who underwent PN for renal cell carcinomas. Methods: Our analysis included patients who underwent PN for renal cell carcinoma in 21 French academic centers (2010–2020). RENAL, PADUA, and SPARE scores were calculated based on preoperative imaging. Uni- and multivariate cox models were performed to identify predictors of recurrence-free survival and overall survival. The area under the curve (AUC) was used to identify models with the highest discrimination. Decision curve analyses (DCAs) determined the net benefit associated with their use. Results: A total of 1927 patients were analyzed with a median follow-up of 32 months (14–45). RENAL score (p = 0.01), age (p = 0.002), histological type (p = 0.001), high nuclear grade (p = 0.001), necrotic component (p < 0.001), and positive margins (p = 0.005) were significantly related to recurrence in multivariate analyses. The discriminative performance of the 3 radiological scores was modest (65, 63, and 63%, respectively). All 3 scores showed good calibration, which, however, deteriorated with time. Decision curve analysis of the three models for the prediction of overall and recurrence-free survival was similar for all three scores and of limited clinical relevance. Conclusion: The association between nephrometry scores and oncological outcomes after NP is very weak. The use of these scores for predicting oncological outcomes in routine practice is therefore of limited clinical value. [ABSTRACT FROM AUTHOR]

Gastric cancer is a kind of tumor with strong heterogeneity. Long noncoding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play significant roles in the development of tumors. In this study, we divided all TCGA gastric cancer patients into the whole, intestinal and diffuse cohorts for further analysis, and constructed competitive endogenous RNA network and evaluated immune cells using CIBERSORTx. The support vector machines recursive feature elimination (SVM-RFE) was used for screening significant signatures and the support vector machines (SVM) for establishing model predicting the lymph node metastasis. The performance of SVM model was good in the intestinal and diffuse cohort, while the model in the whole cohort was relatively poor. Some important co-expression patterns between immune cells and ceRNAs network indicated significant correlation CD70 with dendritic cells and so on. Our research inferred competing endogenous RNA network of lymph node metastasis and built an excellent predicting model. [ABSTRACT FROM AUTHOR]

The purpose of this study was to produce a short-form measure of loneliness and assesses its prediction of depressive symptoms relative to a comprehensive measure. Western Australian adolescents completed the Friendship Related Loneliness and Isolation subscales of the Perth Aloneness Scale (PALs) three times over 18 months (T 1 n = 1538; T 2, n = 1683; T 3, n = 1406). Items were reduced while preserving predictability. Follow-up confirmatory factor analyses and predictive models with the reduced and full PALs were then tested. A reduced six-item scale (PALs-6) preserved the two-factor structure of the PALs and showed strong prediction of very elevated depressive symptoms (Sensitivity = 0.70, Specificity = 0.78, AUC = 0.81); it was less successful in predicting future symptoms (Sensitivity = 0.67, Specificity = 0.64, AUC = 0.74). The PALs-6 provides a brief measure of adolescent loneliness for clinicians and researchers that also predicts very elevated levels of depression. [ABSTRACT FROM AUTHOR]

Predicting severe obstetric complications - hemorrhages - still is an important medical, demographic, and economic problem throughout the world. In the high-tech era, artificial intelligence seems to be a promising tool for dealing with this problem. This work presents results of information search for the actual experience with various methods of predicting obstetric bleedings. Long-term benefits of applying machine learning algorithms for the said purposes have been shown, and their implementation challenges have been covered. Development of digital predictive models backed up with machine learning algorithms is expected to break new ground for enhancing the precision of identifying personalized risk of bleeding. This assumption relies on the successful Russian and worldwide experience in implementing integral systems of predictive analytics into such branches of medicine, as oncology, cardiology, ophthalmology, and reproductive medicine. [ABSTRACT FROM AUTHOR]

BackgroundThis study aims to discern the significance of common hematological and biochemical parameters for predicting urinary tract infections in geriatric patients with hip fractures.MethodsMultivariable logistic regression and propensity score-matched analyses were conducted to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for UTIs. The abilities of these parameters to predict UTIs were evaluated by receiver operating characteristic (ROC) curves. Dose–response relationships were assessed by categorizing hematological and biochemical parameters into quartiles. Subgroup analyses were further explored to investigate the relationship between these parameters and urinary tract infections.ResultsOut of the 1,231 participants, 23.2% were diagnosed with UTIs. Hyperglycemia, hypoproteinemia and hyperglobulinemia were risk factors for UTIs in multivariate analysis. After propensity score matching, hyperglycemia (OR 2.14, 95% CI 1.50–3.05, p

The data were derived from several sources, including 338 records of maturity, age, and disc width of the Spiny butterfly ray (Gymnura altavela). ‎An artificial neural network ‎‎(Multilayer Perceptron) model (1, 10, 2) was used to predict the maturity and age of the ‎ Spiny butterfly ray, which is a high-accurate ‎model with excellent efficiency. This network ‎shortens the time, effort and cost compared to traditional methods or a convolutional neural ‎network (CNN) in age prediction. Therefore, we can predict the maturity and age of ‎individuals without killing or harming them, just by getting simple data (disc width) and ‎inter it into the updated model from the network. This network allows us to obtain valuable ‎data for use in studying stock indicators of ‎endangered Spiny butterfly ray ‎without ‎compromising their factual ‎stock.‎

The paper investigates the growth rates of agriculture as one of the leading industries, which serves as a catalyst for economic development and is intended to provide the population with food products and industry with raw materials. The main aim of the conducted research is to analyze the impact of agricultural reforms on the agricultural growth rates in Algeria. The systematic analysis of literary sources and methods of addressing the problem of agricultural growth indicates the existence of socio-economic, political, and institutional inhibitors of effective agricultural reclamation of the agricultural sector. Five inputs were used as parameters characterizing the development of the agricultural industry. These are the volume of agricultural financing, the volume of exploited agricultural area, the volume of developed agricultural machinery, the volume of agricultural labor, and the rural level of agricultural growth. Forecasting indicators of the development of the agricultural industry was carried out using the methods of min max scaler tool, ReLU, and RMSE. A machine learning model from the Python language built the artificial neural network model. The statistical data of the Ministry of Finance and Agriculture and the World Bank served as the information base for the study of the relationship between the amount of adopted and implemented agrarian reforms. The link between the number of agrarian reforms adopted and expressed was investigated using data from several areas of statistics, the Ministry of Finance and Agriculture, and the World Bank. The modelling results proved the positive influence of bank financing, the volume of exploited agricultural areas and the number of the population employed in agriculture on the growth rates of agriculture in Algeria in the long term. An increase in agricultural mechanization by one unit (tractors and agricultural equipment per 100 km2) contributes to the growth of indicators of agricultural development by 0.21%, and an increase in the number of people employed in agriculture per million will lead to a rise in the level of agricultural development by 12 %. Thus, the results of econometric modelling proved the positive impact of agricultural reforms on the development of agriculture after 2022. It contributes to the increase in the level of employment of the population and, ultimately, will have a positive effect on the future rate of economic growth in Algeria.

The article highlights the problem of forming the prognostic competence of a modern teacher as an integral element of his/her professional competence. The generalization of the study results fixed in scientific literature on the initiated problem made it possible to specify the concept “prediction” (a scientifically substantiated statement about the possible states of the object or alternative ways and terms of their implementation; the result of prognostic activity, anticipatory knowledge, hypothesis, a set of information obtained as a result of the use of human abilities to anticipation, prediction based on intuition and scientific understanding of patterns which are applied to events and phenomena both in the past, present and future time). It was established that the prognostic competence of the teacher is a synthetic, multi-determined personal and professional construct that determines the teacher's ability to coordinate the goal and result of professional and pedagogical activities (pedagogical forecast); to determine the directions of pedagogical activity according to the proposed hypotheses; to enrich the palette of professional functions with pedagogical predicting. It has been proven that the prognostic competence allows the teacher to construct individual educational trajectories of students and predict likely changes in the field of education using the potential of pedagogical prognostication; reflects the teacher's own desire for professional self-determination and personal development of others by means of didactics. The structure of the prognostic competence of a modern teacher is described in the unity of cognitive-informational, intellectual-mental, instrumental-technological and worldview-related professional components. The cognitive-informational component of the prognostic competence provides a theoretical “foundation” that reflects the systematicity, orderliness, and complexity of education seekers’ knowledge regarding the methodology of prognostication, the phasing of prognostic activity, the vectors of pedagogical predicting (forecasting) within the system of professional and pedagogical activities. The intellectual-mental component of the prognostic competence of a Mathematics teacher reflects his/her innate cognitive intentions, the level, rhythm and pace of assimilation of information, ability-related opportunities in the use of higher-order cognitions, mental activity, flexibility of thinking and its variability. The instrumental and technological component of the teacher's prognostic competence records the practical possibilities of the teacher to implement the cognitive-informational profile of pedagogical prognostication in the educational space. It reflects a complex of skills that ensure the effective implementation of the elements of pedagogical forecasting in the system of professional and pedagogical activities. The worldview-related professional component of the teacher’s prognostic competence expresses the teacher’s stable professional position regarding the use of the potential of pedagogical prognostication in the system of specific pedagogical activity. It reflects innate pedagogical abilities that allow a teacher to correctly and creatively use the skills of pedagogical forecasting to build students’ individual educational trajectories at an intuitive level.

Reciprocal teaching (RT) involves collaborative dialogue between the teacher and students, with the aim of jointly constructing the meaning from a shared text. This study employs RT as a cognitive and metacognitive set of strategies for enhancing students’ reading comprehension and monitoring skills. Thirty-four students were assigned to the RT group for reading instruction, whereas 30 students were assigned to the control group for regular reading instruction. The four RT strategies (predicting, questioning, clarifying, and summarizing) empowered the students to engage in a non-linear approach to comprehend texts and monitor their reading comprehension. They afforded the students the chance to engage in pre-, during-, and post-reading cognitive and metacognitive processes, collaborate with their peers, read with intentionality and focus, and assume shared responsibility for monitoring their advancement in reading comprehension. The students engaged in the scaffolded activities of read-aloud, dialogue, structured comprehension, and peer teaching to develop their reading comprehension and monitoring skills. The results showed that the RT group outperformed the control group on the post-test in both total score of reading comprehension and individual reading skills. Therefore, language instructors are recommended to use an array of effective RT strategies to enhance their students’ reading comprehension and monitoring skills.