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This study aims to examine how mothers' poverty differently affects young adult children's self-esteem over time. We consider how young adult children's income moderates the between-subject relationship and the within-subject relationship. This study used two data sets such as the NLSY79 and the NLSY79CY. We used six waves from 2004 to 2014. A total of 3975 mother–adult child dyads were chosen. The hybrid model was employed to explore both the between-subject and the within-subject relationship. There was a significant between-subject relationship regarding the effect of mothers' poverty and adult children's income on adult children's self-esteem. In the between-subjects relationship, an interaction effect was found, such that children's income moderated the between-subject relationship between mothers' poverty and children's self-esteem. Long-term anti-poverty programs should be provided to women with children even after they completely are away from poverty. Minimizing inequality in intergenerational economic mobility may be critical to enhance adult children's self-esteem. [ABSTRACT FROM AUTHOR]

Creep-feed grinding is a high-efficiency, high-precision grinding process widely used in the manufacturing of aviation engines. However, the workpiece burn and other quality issues caused by high processing temperature limit the yield of grinding. Therefore, the accurate model of grinding temperature has become the key to improving processing efficiency and quality. Different from the traditional physical or data-driven models, this paper attempts to combine both perspectives based on Physics-Guided Neural Networks (PGNN) to accurately predict grinding temperature with a small number of experiments. At the level of data acquisition, real grinding experiment data was obtained and a data augmentation method had been proposed. At the level of neural network structure, optimisation processes were implemented to enhance prediction performance, and a physics-guided loss function was inserted to guide network training. The experiment results shows that PGNN had better prediction accuracy than the physical model, while also mitigating the limitations of data-driven models on small sample sets. PGNN also performed better with noisy data and predictions out of the training data range, this reveals the benefits of PGNN for small sample problems in processing scenarios. [ABSTRACT FROM AUTHOR]

Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on improved feature extraction and non-linear integration, which is built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FuzzyEn), improved random forest using particle swarm optimisation (PSORF), extreme learning machine (ELM), long short-term memory (LSTM), non-linear integration based on multiple linear regression (MLR) and random forest (MLRRF), and error correction with the autoregressive integrated moving average model (ARIMA), named CEEMDAN-FuzzyEn-PSORF-ELM-LSTM-MLRRF-ARIMA. Firstly, CEEMDAN is combined with FuzzyEn in the feature selection process to improve extraction efficiency and reliability. Secondly, at the critical prediction stage, PSORF, ELM, and LSTM are selected to predict high, medium, and low complexity sequences, respectively. Thirdly, the reconstructed sequences are assembled by applying MLRRF, which can effectively improve the prediction accuracy and generalisation ability. Finally, error correction is conducted using ARIMA to obtain the final forecasting results, and the Diebold–Mariano test (DM test) is introduced for a comprehensive evaluation of the models. With respect to carbon prices in the pilot regions of Shenzhen and Hubei, the results indicate that the proposed model has higher prediction accuracy and robustness. The main contributions of this paper are the improved feature extraction and the innovative combination of multiple linear regression and random forests into a non-linear integrated framework for carbon price forecasting. However, further optimisation is still a work in progress. [ABSTRACT FROM AUTHOR]

COVID‐19 has been ravaging the world for a long time, and although its effects are currently the same as those of a cold or a fever, timely diagnosis of COVID‐19 in the elderly and in patients with related illnesses is still a matter of great urgency. To address this challenge, we propose a model that combines the strengths of the Swin Transformer and ResNet34 architectures to efficiently diagnose COVID‐19 in elderly and vulnerable patients. In this paper, we design a model that integrates Swin transformer and resnet34, which not only integrates the advantages of transformer and CNN but also achieves excellent performance in this image classification problem. Moreover, a pre‐processing method is also proposed to increase the accuracy of the model to 99.08%. In this paper, experiments were conducted on Kaggle's publicly available three‐classification and four‐classification datasets, respectively, and on the three main evaluation metrics of Accuracy, Precision, and Recall, the first dataset obtained 98.81%, 99.49%, and 97.99%, while the second dataset obtained 88.82%, 88.92%, and 86.38%. These findings highlight the validity and potential of our proposed model for diagnosing the presence or absence of COVID‐19 in elderly and vulnerable patients. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) assessment methods relying on physical and empirical models are considered to be the most commonly used method at present, but their estimation efficiency and accuracy are deficient. Although the hybrid model of these two methods can address these issues, a poor coupling mechanism can easily occur. Given that, a PROSAIL model coupling particle swarm optimization (PSO) neural network (NN) algorithm (PSO-NN-PROSAIL model) was introduced to invert the winter wheat LAI (WWLAI) at five distinct growth stages. The Xiangfu District in the east of Kaifeng City, Henan Province was served as the study region. Based on the measured WWLAI data at varying stages and GF-1 WFV satellite images, the initial analysis focused on assessing the PROSAIL model's sensitivity in simulating vegetation canopy reflectance. It then calculated six vegetation index models according to the wavelength reflectance of GF-1 WFV and analysed their correlation with LAI to select the input parameters that could be used in the model. Normalized differential vegetation Index (NDVI) and ratio vegetation Index (RVI) as well as vegetation canopy reflectance were employed as input parameters to invert the WWLAI by adopting the PSO-NN-PROSAIL model. The experimental results showed the following: (1) In the vegetation index model, the determination coefficient (R2) of NDVI and RVI was greater than 0.68, implying that NDVI and RVI might serve as input factors for the proposed model in this paper; (2) LAI and chlorophyll a + b content (Cab) were most sensitive to the PROSAIL model in near-infrared and visible light bands; and (3) the PSO-NN-PROSAIL model possessed better LAI inversion accuracy. In summary, the model proposed in this paper provided a technical reference for rapid and accurate remote sensing monitoring of WWLAI. The optimization technology of particle swarm optimization algorithm is integrated into the neural network, and the weight of the neural network is adjusted by its fitness function transformation and inertia weight. By integrating prior knowledge into the inversion of crop leaf area index constructed by machine learning and radiative transfer model, the ill-posed problem of physical model inversion has been improved. [ABSTRACT FROM AUTHOR]

Cyanobacterial harmful algal blooms (cyanoHABs) caused by cyanobacteria negatively affect humans via river water and aquatic life. Thus, reliable cyanobacteria predictions are essential for managing cyanoHABs. With recent advancements in computer technology and big data usage, artificial intelligence (AI) technologies have gained attention in various fields, such as water resources, weather and climate, and water quality. This study evaluated the applicability of deep-learning-based AI technology for predicting cyanobacteria. A convolutional neural network (CNN)–long short-term memory (LSTM) model, a deep-learning-based AI technology advantageous for predicting time-series data and cyanobacteria features, was built. Its results were analyzed and compared with those of the existing physical Environmental Fluid Dynamics Code (EFDC)–National Institute of Environment Research (NIER) model for cyanobacteria prediction. The CNN-LSTM model performed better, with an accuracy of 69%, which is an improvement over the previous EFDC-NIER model's accuracy of 45%. In particular, there was a dramatic improvement in the prediction accuracy for low cyanobacteria cell counts in Level 1, which increased from 39% to 87%. There also was an improvement in the prediction accuracy for Levels 2 and 3. The accuracy for Level 2 increased from increased from 56% to 69%, and the accuracy for Level 3 increased from 38% to 48%. However, there was a significant decrease in prediction accuracy for high cyanobacteria cell counts in Level 4, for which the measured data were very scarce; accuracy decreased from 49% to 16.7%. The CNN-LSTM model yielded better overall prediction performance than the EFDC-NIER model, demonstrating its applicability in cyanobacteria prediction. However, it has limitations of overfitting areas with inadequate data and not accurately predicting patterns that have not occurred in the past. To address this issue, we propose an approach the combines the advantages of physics-based models and AI-based deep learning models, creating a hybrid concept. [ABSTRACT FROM AUTHOR]

Purpose Methods Findings Conclusion Implications for nursing practice Clarifying the concept of elder self‐neglect.Researchers investigated the concept of elder self‐neglect using a hybrid approach, including theoretical, fieldwork, and final analysis phases. After an extensive review of related literature to define the concept of self‐neglect and describe its characteristics, the fieldwork phase was carried out to illustrate its empirical features. Accordingly, it led to the extraction of the elder self‐neglect attributes from the first two phases.Elder self‐neglect includes “subjective and objective high‐risk behaviors pertinent to physical and spiritual health as well as restricted social interactions, disregarding the living environment, and a lack of compliance to personal hygiene routines that the elderly demonstrate deliberately or unintentionally. Various factors can contribute to such behaviors, including individual features, poor physical performance, elderly‐oriented psychological disorders, financial challenges, detrimental stressors, unsuccessful social interactions, inaccessible supportive resources, inadequate educational resources, and inappropriate cultural norms. Consequently, such behaviors result in impaired physical and psychological health status, higher risk of misbehavior, poor quality of life, extra caregiving‐treatment burden, and affected mental security in society.”Given that elder self‐neglect is considered a relatively unknown concept in Iran, the study findings can pave the way for future extensive research in the Iranian context by clarifying the concept. It can play a significant role in developing related tools to design more efficient interventions and improve the quality of nursing care services.The elder self‐neglect concept can lead to numerous health‐related disorders among the elderly in society. Nurses cannot deal with an unknown phenomenon without exploring and identifying its exact meaning. It is, therefore, imperative to illustrate the different dimensions of this concept to help create a deep understanding among the nurses and perform timely diagnosis and interventional procedures. [ABSTRACT FROM AUTHOR]

• Proposed-1: Forecast of Solar Cycle 25 using Hybrid CNN-Bidirectional-GRU(CNN-BiGRU). • Proposed-2: Novel Gradient Residual Correction (GRC) model to enhance the proposed-1. • Models are compared with deep-learning traditional models over 4-types of SSN data. • Models' performances are validated based on Friedman ranking for measures. In this study, a hybrid deep-learning(DL) model is proposed, which consists of a Convolution-Neural-Network (CNN) and Bidirectional-Gated-Recurrent-Unit (Bi-GRU) for the prediction of sunspot numbers(SSN) of different frequencies along with novel post-processing techniques of Gradient-Residual-Correction (GRC) to enhance the accuracy of the predictions further. GRC is utilized to reduce the residual present in the forecasted values. The AdaBoost regression model is implemented over the residual obtained from the prediction of training data using a hybrid CNN-BiGRU model with respect to the gradient of the training data points to predict the residual for the test data points. Ultimately, the predicted residual for test data points is summed up with the predicted test data points to achieve the final predictions. The results obtained from the proposed methods are compared with the results obtained from traditional DL models. The validation of the proposed method is carried out based on four performance metrics, namely Root Mean Squared Error(RMSE), Mean Absolute Scaled Error(MASE), Mean Absolute Error(MAE), and Mean Absolute Percentage Error(MAPE). A significant percentage of improvement is observed while using the GRC technique in comparison to all other experimented models for all four variants of SSN data. A maximum improvement of 85.71% has been achieved in comparison to the BiLSTM model over the 13-month smoothed SSN dataset on the basis of MAPE. Friedman Ranking is also performed as a non-parametric statistical test over the results of the performance measures. This model has been utilized for the forecast of solar cycle 25(SC25) over the annual mean of total SSN. It has been observed that the SC25 is expected to reach its peak in the year 2024 with an annual average peak value of 143.641. Comparative analysis of SC25 and the peak of SSN in the ongoing cycle with the previous works are also carried out. [ABSTRACT FROM AUTHOR]

At present, the electronic nose has became a new technology for the rapid detection of pesticides. However, the technique may misidentify them for samples that have not been involved in training. Therefore, a hybrid model based on unsupervised and supervised learning was proposed for the first time in this paper. The model divided the detection process of soil pesticide residues into two steps: (1) an unsupervised machine learning method was used to identify whether the soil was contaminated with pesticides; (2) when the soil was contaminated with pesticides, a supervised classifier was further used to predict the types of pesticides in the soil. The experimental results showed that the model had a recognition accuracy of 99.3% and 99.27% for whether the soil was contaminated with pesticides and the pesticide type of the contaminated soil, respectively, with a detection time of 0.03 s. The results revealed that the proposed hybrid model can quickly and comprehensively reflect the soil information's status. [ABSTRACT FROM AUTHOR]

Utilizing identical climate indices as predictors for all climate divisions within large basins may result in unreliable rainfall forecasts at the sub-basin scale. This study aimed to develop a new approach to identify the most effective predictors among large-scale climate indices for seasonal rainfall forecasting in small areas. The proposed approach combines a selective singular value decomposition method (SSVD) with a non-linear sequential forward selection method (NLSFS). Applying the new algorithm for seasonal rainfall forecasting within two climate divisions in Karkheh basin, Iran, indicated that the climate indices identified by the SSVD differed between the study areas. The combination of these indices exhibited a correlation with seasonal rainfall approximately 11% higher than those derived from the SVD method. Moreover, NLSFS significantly enhanced the forecast accuracy compared to the frequently employed linear sequential forward selection (SFS) method, and the optimal predictors chosen by the two methods differed across all seasons. [ABSTRACT FROM AUTHOR]

The rapid advancement of industrialization and urbanization has led to the global problem of air pollution. Air quality can decrease due to pollutants in the air, including types of gases and particles that are carcinogenic, causing adverse health effects. Therefore, estimating the concentration of air pollutants is of great interest as it can provide accurate information about air quality with proper planning of future activities. In this manner, this study considers Istanbul, a province with a high concentration of industry, population, and vehicle traffic. Particulate matter (PM), one of the most basic air pollutants, is stated to contain microscopic solids or liquid droplets that are small enough to be inhaled and cause serious health problems. Thus, it is recommended to apply discrete wavelet transform (DWT) and deep learning method long short‐term memory (LSTM) as a hybrid model to predict the concentration of PM10. Using the mentioned methods, they can predict air pollution to have been developed within the scope of this study. Furthermore, the hybrid approach with LSTM by selecting the most appropriate discrete wavelet type emphasizes the difference of this study from the existing literature. The ability of these developed methods to make successful future predictions helps institutions and organizations that can take precautions on the subject to take action at the right time; in addition, the deep learning methods used contribute to the development of sustainable smart environmental systems. In today's environment when air pollution is increasing and threatening human health, any precaution that can be taken would improve the quality of life for all living things, reduce health issues and deaths caused by air pollution, and thus raise the degree of well‐being. These findings might offer a reliable scientific evidence for Istanbul City's air pollution management, which can serve as an example for other regions. This study investigates the importance of wavelet type selection as a determinant factor in the performance of DWT‐LSTM models. Among the wavelet types considered, the Db10 from the Daubechies wavelet family consistently emerges as a standout performer. A compelling narrative of superior performance unfolds in evaluating the hybrid DWT‐LSTM model against the LSTM method. The DWT‐LSTM model exhibits a notable reduction in error metrics and captures intricate patterns and nuances within the PM10 concentration data indicating its robust predictive capabilities. [ABSTRACT FROM AUTHOR]

Anomaly detection, a critical aspect of data analysis and cybersecurity, aims to identify unusual patterns that deviate from the expected norm. In this study, we propose a hybrid approach that combines the strengths of Autoencoder neural networks and Multiclass Support Vector Machines (SVM) for robust anomaly detection. The Autoencoder is utilized for feature learning and extraction, capturing intricate patterns in the data, while the Multiclass SVM provides a discriminative classification mechanism to distinguish anomalies from normal patterns. Specifically, the Autoencoder is trained on normal data to acquire a compact and efficient representation of the underlying patterns, with the reconstruction errors serving as indicative measures of anomalies. Concurrently, a Multiclass SVM is trained to classify instances into multiple classes, including an anomaly class. The anomaly scores from the Autoencoder and the decision function of the Multiclass SVM, along with that of the Random Forest Neural Network (AE-RFNN), are combined, leveraging their complementary strengths. A thresholding mechanism is then employed to classify instances as normal or anomalous based on the combined scores. The performance of the hybrid model is evaluated using standard metrics such as precision, recall, F1-score, and the area under the Receiver Operating Characteristic (ROC) curve. The proposed hybrid anomaly detection approach demonstrates effectiveness in capturing complex patterns and discerning anomalies across diverse datasets. Additionally, the model offers flexibility for adaptation to evolving data distributions. This study contributes to the advancement of anomaly detection methodologies by presenting a hybrid solution that combines feature learning and discriminative classification for improved accuracy and generalization. [ABSTRACT FROM AUTHOR]

As an emerging force in the travel industry, online car-hailing (OCH) has been relying on digital and intelligent technologies for business innovation since its birth. Three hybrid deep learning models combining multi-factor external and internal features are proposed to predict the OCH demand. Convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and convolutional LSTM (ConvLSTM) are selected to extract features. Attention mechanisms are used to combine the global parts so that the importance of feature sequences at different times can be distinguished. The effectiveness of the proposed models considering all factors is proved by comparative experiments. Then, ablation experiments are performed to analyze the effects of attention module, external and internal factors. The results showed that the hybrid models performed better than the existing models under different factors. Various factors had different impacts on the departure and arrival flows and the hybrid models. [ABSTRACT FROM AUTHOR]

Hybrid neural network models are effective in analyzing time-series data by combining the strengths of neural networks and differential equation models. Although most studies have focused on linear hybrid models, few have examined nonlinear problems. This work explores the potential of a hybrid nonlinear epidemic neural network in predicting the correct infection function of an epidemic model. We design a novel loss function by combining bifurcation theory and mean-squared error loss to ensure the trainability of the hybrid model. Additionally, we identify unique existence conditions that support ordinary differential equations for estimating the correct infection function. Moreover, numerical experiments using the Runge–Kutta method confirm our proposed model's soundness both on our synthetic data and the real COVID-19 data. [ABSTRACT FROM AUTHOR]

Malaria is a vector-borne disease that exacts a grave toll in the Global South. The epidemiology of Plasmodium vivax, the most geographically expansive agent of human malaria, is characterised by the accrual of a reservoir of dormant parasites known as hypnozoites. Relapses, arising from hypnozoite activation events, comprise the majority of the blood-stage infection burden, with implications for the acquisition of immunity and the distribution of superinfection. Here, we construct a novel model for the transmission of P. vivax that concurrently accounts for the accrual of the hypnozoite reservoir, (blood-stage) superinfection and the acquisition of immunity. We begin by using an infinite-server queueing network model to characterise the within-host dynamics as a function of mosquito-to-human transmission intensity, extending our previous model to capture a discretised immunity level. To model transmission-blocking and antidisease immunity, we allow for geometric decay in the respective probabilities of successful human-to-mosquito transmission and symptomatic blood-stage infection as a function of this immunity level. Under a hybrid approximation—whereby probabilistic within-host distributions are cast as expected population-level proportions—we couple host and vector dynamics to recover a deterministic compartmental model in line with Ross-Macdonald theory. We then perform a steady-state analysis for this compartmental model, informed by the (analytic) distributions derived at the within-host level. To characterise transient dynamics, we derive a reduced system of integrodifferential equations, likewise informed by our within-host queueing network, allowing us to recover population-level distributions for various quantities of epidemiological interest. In capturing the interplay between hypnozoite accrual, superinfection and acquired immunity—and providing, to the best of our knowledge, the most complete population-level distributions for a range of epidemiological values—our model provides insights into important, but poorly understood, epidemiological features of P. vivax. [ABSTRACT FROM AUTHOR]

Due to the nonlinearity and high volatility of financial time series, hybrid forecasting systems combining linear and nonlinear models can provide more precise performance than a single model. Therefore, this study proposes a hybrid stock price forecasting model with error correction based on secondary decomposition. The modules of data decomposition, a prediction module and an error correction module constitute the overall framework of the model proposed in this paper. First, variational mode decomposition (VMD) decomposes the original stock closing price data and the initial prediction error sequence. Second, a gated recurrent unit (GRU) neural network model is selected to predict the original subseries. Finally, a new hybrid model consisting of the VMD method combined with an autoregressive integrated moving average (ARIMA) model is established to correct the error subseries. The proposed model is verified through four financial time series. The results show that (a) The combination of the VMD method and GRU network has a better prediction effect than a single model. (b) The combination of the VMD method and ARIMA method can effectively amend the error sequence and improve the prediction accuracy. (c) Error correction to the hybrid model has a great effect on improving the prediction performance of the model. The empirical results have illustrated that the proposed model indeed displays a good performance in forecasting stock market fluctuations compared with other baseline methods. [ABSTRACT FROM AUTHOR]

Rate of penetration (ROP) is a key factor in drilling optimization, cost reduction and drilling cycle shortening. Due to the systematicity, complexity and uncertainty of drilling operations, however, it has always been a problem to establish a highly accurate and interpretable ROP prediction model to guide and optimize drilling operations. To solve this problem in the Tarim Basin, this study proposes four categories of hybrid physics-machine learning (ML) methods for modeling. One of which is residual modeling, in which an ML model learns to predict errors or residuals, via a physical model; the second is integrated coupling, in which the output of the physical model is used as an input to the ML model; the third is simple average, in which predictions from both the physical model and the ML model are combined; and the last is bootstrap aggregating (bagging), which follows the idea of ensemble learning to combine different physical models' advantages. A total of 5655 real data points from the Halahatang oil field were used to test the performance of the various models. The results showed that the residual modeling model, with an R2 of 0.9936, had the best performance, followed by the simple average model and bagging with R2 values of 0.9394 and 0.5998, respectively. From the view of prediction accuracy, and model interpretability, the hybrid physics-ML model with residual modeling is the optimal method for ROP prediction. [ABSTRACT FROM AUTHOR]

AbstractThe period of reentering society after prison is a critical time in the life-course and presents numerous challenges. With whom and where individuals live can determine aspects of social support, as well as bonds and informal social control that may predict whether a person “succeeds” by abstaining from crime and avoiding technical violations of parole. We use detailed administrative data from a release cohort in Missouri to construct monthly variation in household and community context at two levels (neighborhood disadvantage and county-level rural-urban designation). We follow the local-life circumstances approach, decomposing changing context into within and between person variation (Horney et al., 1995). We find important selection effects but also intervention effects – with households including family as particularly beneficial. This work further highlights the needs for multiple measures of recidivism and a broadening of the geographic locales researchers and policymakers consider. [ABSTRACT FROM AUTHOR]

Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid–liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited, because fixed recipes are usually applied to run the processes. In order to take the varying properties of biological feed materials into account, we aim to develop a digital twin of an industrial brownfield DCF plant, allowing to optimize setpoint decisions in almost real time. The core of the digital twin is a mechanistic–empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation in the selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach, and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. We considered a total of 24 industrial production batches from the filtration of grape juice from the years 2022 and 2023 in the study. After implementation of the digital twin on site, the campaign mean productivity increased by 15% over the course of the year 2023. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes. [ABSTRACT FROM AUTHOR]

Rock burst is one of the major disasters that endanger coal safety production. If a rock burst occurs, it will cause terrible casualties and significant property losses. Therefore, this article proposes to predict the sensitive characteristics of microseisms, which can achieve the prediction and early warning of rock burst disasters to a certain extent. To effectively improve the prediction accuracy and robustness of microseismic sensitive feature data, a hybrid model called VMD-Transformer is suggested in this study for predicting time series of microseismic sensitive features. This model is based on the variational mode decomposition (VMD) and transformer model and aims to predict future eigenvalue from the historical data of sensitive features. To a certain extent, the transformer model is used to predict the future eigenvalue, while the VMD is used to extract the features of the time series data at various frequency domain scales, which solves the problem of non-stationary time series data being difficult to predict accurately due to high fluctuations. This study extracts sensitive features from microseismic events that the same source registered by a certain geophone after locating, decomposes the time series of the sensitive features using VMD, predicts each component of the decomposition separately using the transformer model, and then combines the component prediction results to produce the final prediction results. Experimental results indicate that our method has the features of a simple algorithm, strong adaptivity, and high prediction accuracy and can effectively predict time series of sensitive features extracted from microseismic signals. [ABSTRACT FROM AUTHOR]

Dual‐frequency (DF) dual‐antenna inductively coupled plasma (ICP) sources have been proposed as one of the methods to produce large‐area uniform plasma. In this study, a hybrid model, consisting of a fluid module and an electron Monte Carlo (eMC) module, is used to further investigate the modulation of the plasma characteristics (i.e., induced electric field, electron energy distribution, electron heating mechanism, electron temperature, and plasma density) by the low‐frequency (LF) and high‐frequency (HF) currents in a DF argon discharge. When the inner LF current increases from 12 to 22 A at a fixed outer HF current of 10 A, the induced electric field is strengthened near the LF coil and weakened near the HF coil. The fraction of high‐energy electrons at different spatial positions increases with the increase of LF current, which is caused by the sufficient collision heating in the skin layer of LF source, leading to an increase in electron temperature. When the outer HF current rises from 7 to 13 A with the inner LF current fixed at 17 A, the induced electric field is enhanced near the HF coil and weakened near the LF coil. The fraction of low‐energy electrons at different positions increases with the increase of HF current since the energetic electrons from the skin layer of HF source are cooled due to the ionization, excitation, and stepwise ionization collisions between electrons and neutral particles, as well as the weakened induced electric field near the LF coil. As a result, the electron temperature drops with the increase of HF current. Moreover, when the LF and HF currents increase, the plasma density increases, and the distribution of plasma density is also significantly modulated. [ABSTRACT FROM AUTHOR]

pH adjustment is crucial for many industrial products, yet this step is typically performed by manual trial‐and‐error. A particularly industrially relevant yet challenging titration is that of adjusting viscous liquid formulations using weak, polyprotic titrants (usually citric acid). Handling of viscous, non‐Newtonian formulations, with such polyprotic acids preferred for their chelation and buffering effects make a robotic solution challenging. We present a self‐driving pH robot integrated with physics‐informed learning; this hybrid physical‐ML model enables automated titration with weak‐strong acid/base pairs. To deal with the high viscosities of these formulations, we developed specific automated mixing and cleaning protocols. We hit the target pH within two to five iterations over 250 distinct formulations in lab‐scale small‐batch (~10 mL and 12 samples) titrations. In the interest of scaling up to match industrial processes, we also demonstrate that our hybrid algorithm works at ~25× scale‐up. The method is general, and we open‐source our algorithm and designs. [ABSTRACT FROM AUTHOR]

AbstractEmotions are synchronizing responses of human brain while executing cognitive tasks. Earlier studies had revealed strong correlation between specific lobes of the brain to different types of emotional valence. In the current study, a comprehensive three-dimensional mapping of human brain for executing emotion specific tasks had been formulated. A hybrid computational machine learning model customized from Custom Weight Allocation Model (CWAM) and defined as Custom Rank Allocation Model (CRAM). This regression-based hybrid computational model computes the allocated tasks to different lobes of the brain during their respective executive stage. Event Related Potentials (ERP) were obtained with significant effect at P1, P2, P3, N170, N2, and N4. These ERPs were configured at Pz, Cz, F3, and T8 regions of the brain with maximal responses; while regions like Cz, C4 and F4 were also found to make effective contributions to elevate the responses of the brain, and thus these regions were configured as augmented source regions of the brain. In another circumstance of frequent –deviant - equal (FDE) presentation of the emotional stimuli, it was observed that the brain channels C3, C4, P3, P4, O1, O2, and Oz were contributing their emotional quotient to the overall response of the brain regions; whereas, the interaction effect was found presentable at O2, Oz, P3, P4, T8 and C3 regions of brain. The proposed computational model had identified the potential neural pathways during the execution of emotional task. [ABSTRACT FROM AUTHOR]

The model for online prediction of product yield embedded in the optimization and control framework has the potential to improve the profits of fluid catalytic cracking (FCC) units. In this work, a hybrid soft sensor model is developed to predict key product yield in commercial FCC units. The deep learning framework based on semi-supervised learning is driven by data and process mechanism to construct the hybrid model with a double-layer structure. Moreover, process simulation strategy is utilized for key input variable selection of the hybrid model to ensure model reliability. The hybrid model is compared to a pure data-driven model in terms of accuracy and trend consistency of model predictions. The results demonstrate that the hybrid model exhibits a superior prediction performance. This work suggests that the hybrid model can drive innovation and industrial practice in the optimization and control of FCC units. [Display omitted] • Online prediction of product yield for the FCC unit with a hybrid soft sensor model based on a deep learning framework. • Excellent generalization of the hybrid model with a double-layer structure. • Novel strategy for variable selection as the input of the soft sensor. [ABSTRACT FROM AUTHOR]

The concept of models-based practice has arguably provided the field of sport pedagogy with the means, lens, and tools with which to start to transform the discipline into a more desirable, equitable, and purposeful space. Despite this, there remains a need to experiment with this concept further in non-traditional ways and to continue to construct new pedagogical models meant to serve youth in ever-changing pluralist societies. The purpose of this paper, therefore, was to outline an affective-oriented composite pedagogical model, titled The Spectrum Model, by drawing from Mosston and Ashworth's theory for the spectrum of teaching and learning and neo-Aristotelian virtue ethics. To achieve this goal, I begin by highlighting the need for studying the synergy between the spectrum and models-based practice more closely. Thereafter, I attempt to clarify spectrum theory, virtue ethics, and the concept of models-based practice. Finally, I conclude by presenting two broad main ideas, three critical elements, six intended learning aspirations, and a series of suggested pedagogies acting to inform the proposed Spectrum Model. This model has the potential to influence how practitioners and scholars understand the spectrum of teaching and learning within the broader notion of models-based practice. The application and integration of moral philosophy into a well-established pedagogical framework might also help pedagogues of all kinds to envision how they could help youth to obtain a fully flourishing lifestyle through their teaching. [ABSTRACT FROM AUTHOR]

The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error. [ABSTRACT FROM AUTHOR]

Sub-hourly load forecasting can provide accurate short-term load forecasts, which is important for ensuring a secure operation and minimizing operating costs. Decomposition algorithms are suitable for extracting sub-series and improving forecasts in the context of short-term load forecasting. However, some existing algorithms like singular spectrum analysis (SSA) struggle to decompose high sampling frequencies and rapidly changing sub-hourly load series due to inherent flaws. Considering this, we propose an empirical mode decomposition-based hybrid model, named EMDHM. The decomposition part of this novel model first detrends the linear and periodic components from the original series. The remaining detrended long-range correlation series is simplified using empirical mode decomposition (EMD), generating intrinsic mode functions (IMFs). Fluctuation analysis is employed to identify high-frequency information, which divide IMFs into two types of long-range series. In the forecasting part, linear and periodic components are predicted by linear and trigonometric functions, while two long-range components are fitted by long short-term memory (LSTM) for prediction. Four forecasting series are ensembled to find the final result of EMDHM. In experiments, the model's framework we propose is highly suitable for handling sub-hourly load datasets. The MAE, RMSE, MARNE, and R2 of EMDHM have improved by 20.1%, 26.8%, 22.1%, and 5.4% compared to single LSTM, respectively. Furthermore, EMDHM can handle both short- and long-sequence, sub-hourly load forecasting tasks. Its R2 only decreases by 4.7% when the prediction length varies from 48 to 720, which is significantly lower than other models. [ABSTRACT FROM AUTHOR]

We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models. [ABSTRACT FROM AUTHOR]

This study was conducted for six cities in southern Algeria, where the accuracy of three models--support vector machines (SVM), artificial neural networks (ANN) and a novel hybrid firefly algorithm-based model (FFA-ANN)--were investigated when estimating global solar irradiation throughout an eleven-year period, utilizing nine input parameters as input data. The goal of our novel suggested a hybrid FFA-ANN model, where we relied on the optimization Firefly algorithm to enhance the ANN model created. Despite the fact that the ANN and SVM models produced promising results, our suggested FFA-ANN hybrid model outperformed the stand-alone ANN-based model using three statistical factors--correlation coefficient, relative root mean squared error and mean absolute percent error--with the best values of (R = 0.9321, rRMSE = 9.35% and MAPE = 6.29%). The findings demonstrated that FFA-ANN was preferable to the optimized SVM and ANN models when forecasting daily global solar irradiation in all zones. Furthermore, after comparing the combinations, the study's findings showed that the ANN model depended on: Extraterrestrial solar irradiation (H0), declination and average temperature (Tavg) together with relative humidity (RH) as inputs in order to estimate daily sun radiation. Thus, the findings of this study suggest that in regions with dry climates and other places with comparable climates, the created model may be used to estimate daily global solar radiation whenever data is accessible. [ABSTRACT FROM AUTHOR]

Predicting sunspot numbers presents ongoing challenges in forecasting, including non-stationary patterns and unclear fluctuation dynamics. This study compares traditional methods and hybrid models, incorporating machine learning techniques, to predict monthly mean sunspot numbers (MMSNs) from January 1, 1900, to December 31, 2022. Among the traditional methods, ARIMA(5,0,4) demonstrated performance with an MSE of 580.949, RMSE of 24.103, MAE of 17.19, and MAPE of 0.511. However, the proposed hybrid model, which combines ARIMA(5,0,4) with additive regression (AR) using Regression by Discretization (RegbyDisc) based on J48, achieved markedly superior forecasting accuracy with an MSE of 114.653, RMSE of 10.708, MAE of 6.441, and MAPE of 0.438. We employed this hybrid model to forecast sunspot numbers from January 2023 to December 2025. [ABSTRACT FROM AUTHOR]

Background: Hand, foot, and mouth disease (HFMD) is a common infectious disease that poses a serious threat to children all over the world. However, the current prediction models for HFMD still require improvement in accuracy. In this study, we proposed a hybrid model based on autoregressive integrated moving average (ARIMA), ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) to predict the trend of HFMD. Methods: The data used in this study was sourced from the National Clinical Research Center for Child Health and Disorders, Chongqing, China. The daily reported incidence of HFMD from 1 January 2015 to 27 July 2023 was collected to develop an ARIMA-EEMD-LSTM hybrid model. ARIMA, LSTM, ARIMA-LSTM and EEMD-LSTM models were developed to compare with the proposed hybrid model. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were adopted to evaluate the performances of the prediction models. Results: Overall, ARIMA-EEMD-LSTM model achieved the most accurate prediction for HFMD, with RMSE, MAPE and R2 of 4.37, 2.94 and 0.996, respectively. Performing EEMD on the residual sequence yields 11 intrinsic mode functions. EEMD-LSTM model is the second best, with RMSE, MAPE and R2 of 6.20, 3.98 and 0.996. Conclusion: Results showed the advantage of ARIMA-EEMD-LSTM model over the ARIMA model, the LSTM model, the ARIMA-LSTM model and the EEMD-LSTM model. For the prevention and control of epidemics, the proposed hybrid model may provide a more powerful help. Compared with other three models, the two integrated with EEMD method showed significant improvement in predictive capability, offering novel insights for modeling of disease time series. [ABSTRACT FROM AUTHOR]

Existing forecasting methods employed for rainfall forecasting encounter many limitations, because the difficulty of the underlying mathematical proceeding in dealing with the patterning and imitation of rainfall data. This study attempts to provide a robust methodology for detecting the nonlinearity of the rainfall pattern by integrating several optimizer algorithms with an Artificial Neural Network (ANN). The Artificial Bee Colony, Particle Swarm Optimization, and Imperialism Competitive Algorithm have been integrated to improve and optimize the internal parameters of theANN method. In Malaysia, a real-world case study was set up, and the ANN model was created using 54 years (1967-2020) worth of local monthly data. The created artificial neural network method is being utilized for rainfall forecasting in real-time. A variety of network types were evaluated with various input information types with the goal of producing accurate rainfall forecasts. Statistical analysis was conducted using various statistical indicators to evaluate the model's accuracy in forecasting rainfall. The study revealed that the model based on the integration of the Imperial Competitive Algorithm with Artificial Neural Network (ICA-ANN) outperformed other predictive models. The results confirmed that the proposed model (ICA-ANN) is a promising predictive model for forecasting monthly rainfall with high accuracy. [ABSTRACT FROM AUTHOR]

Precisely forecasting air temperature as a significant meteorological parameter has a critical role in environment quality management. Hence, this study employs a hybrid intelligent model for accurately monthly temperature forecasting for one to three times ahead in the hottest and coldest regions of the world. The hybrid model contains the artificial neural network (ANN) hybridized with the powerful hetaeristic Honey Badger Algorithm (HBA-ANN). The average mutual information (AMI) technique is employed to find the optimal time delay values for the temperature variable for different time horizons. Finally, the performance of the developed hybrid model is compared with the classical ANN and the Gene Expression Programming (GEP) using some statistical criteria, and the Taylor and scatter diagrams. Results indicated that in each time horizon, the HBA-ANN model with the lowest distance from observation points based on Taylor diagram, high values for NSE and R², and low values for RMSE, MAE, and RSR outperformed the ANN and GEP models in both training and testing phases. Hence, using the Honey Badger Algorithm could increase the accuracy of the model. This model's precise performance supports the case for it to be employed to forecast other environmental parameters. [ABSTRACT FROM AUTHOR]

With the recent trend toward larger floating offshore wind turbines (FOWT), the influence of blade deformation becomes more obvious. Modelling this fully coupled fluid-structure interaction (FSI) system, especially at extreme sea level with large-scale motion of the entire system, is an essential challenge. A hybrid numerical model integrating the potential-viscous flow model (qaleFOAM), the unstable actuator line method (UALM), the Legendre spectral finite element model (BeamDyn), and the Lumped Mass Mooring Model (MoorDyn) is employed in the current research. This model is capable of effortlessly and accurately reflecting the blade aeroelastic effect on the dynamics of a FOWT system under the circumstances of a focused wave and uniform wind. The result reveals that the aeroelastic impacts have been identified to not only increase the fatigue damage on the turbine blades but also affect the wake field distribution. Based on the temporal and spatial distribution of the velocity fields, it is found that the coupled effects of the large wave elevation and the FOWT highfrequency motions will disturb the velocity in the wake region. The slight variation of the wind speed in the wake region will be exacerbated by about 0.21% to 18.6%, and it transforms the shape of the wake region when the blade aeroelastic effect participates in the coupled effect. This phenomenon may further affect the downstream FOWT system features through the upstream FOWT wake field. [ABSTRACT FROM AUTHOR]

This article proposes a method for determining the p-value of correlations in compositional data, i.e., those data that arise as a result of dividing original values by their sum. Data organized in this way are typical for many fields of knowledge, but there is still no consensus on methods for interpreting correlations in such data. In the second decade of the new millennium, almost all newly emerging methods for solving this problem have become based on the log transformation of data. In the method proposed here, there are no log transformations. We return to the early stages of attempting to solve the problem and rely on negative shifts in correlations in the multinomial distribution. In modeling the data, we use a hybrid method that combines the hypergeometric distribution with the distribution of any other law. During our work on the calculation method, we found that the number of degrees of freedom in compositional data measures discretely only when all normalizing sums are equal and that it decreases when the sums are not equal, becoming a continuously varying quantity. Estimation of the number of degrees of freedom and the strength of its influence on the magnitude of the shift in the distribution of correlation coefficients is the basis of the proposed method. [ABSTRACT FROM AUTHOR]

By analysing the flow field of the bipolar plate, considering the inlet and outlet direction, increasing the inclined baffle and the number of channels, an improved multi-channel serpentine flow channel on the opposite side of double baffles is obtained based on the traditional serpentine flow channel. And the hydrodynamic model, electrochemical model and equivalent circuit model are coupled into a hybrid model to obtain the relationship between pump loss current changes during charging and discharging. The experimental results show that the reaction time of the change curve is 52 s earlier and the charging cut-off voltage is 0.3 V higher in the charging and discharging state. [ABSTRACT FROM AUTHOR]

In this paper, a viable, robust, and highly accurate additive hybrid model employing autoregressive fractionally integrated moving average (ARFIMA) and support vector machine (SVM) with functionally expanded inputs (Additive‐ARFIMA‐SVM) is presented for forecasting the air quality index (AQI). Additionally, thirteen additive and multiplicative hybrid models are introduced. Several alternatives in feature engineering employing functional expansion of inputs are incorporated to boost the performance of hybrid models. Furthermore, a gradient whale optimization algorithm with group best leader strategy (GWOA‐GBL) based meta‐heuristic algorithm is proposed. The missing values are imputed and a variable weight ensemble forecasting model is developed using the proposed GWOA‐GBL algorithm. To evaluate the effectiveness of the proposed Additive‐ARFIMA‐SVM forecasting model with functionally expanded inputs, comparisons are made with sixteen machine learning models, including long short‐term memory (LSTM), five statistical models, seventeen hybrid models, and ten variable weight ensemble models. Extensive statistical analyses are carried out on the obtained results considering four accuracy measures that show the statistical supremacy of the proposed Additive‐ARFIMA‐SVM model and GWOA‐GBL algorithm in predicting the AQI time series. The proposed Additive‐ARFIMA‐SVM model with functionally expanded inputs improves the AQI forecasting performance by 16.34% than autoregressive integrated moving average, 14.47% than ARFIMA, 33.96% than XGBoost, 43.47% than SVM, 49.39% than LSTM, 8.64% than Multiplicative‐ARIMA‐SVM model considering symmetric mean absolute percentage error. The proposed Additive‐ARFIMA‐SVM model is so efficient and reliable that it can be applied to forecast other time series like stock price, electricity load, crude oil price, sunspot number, stream flow, flood, drought etc. [ABSTRACT FROM AUTHOR]

The hybrid fluid model for calculating electron transport in air is presented. Within the model, the transport of electrons with energies less than 300 eV is calculated based on the drift-diffusion equation, and in the high-energy range, it is calculated using a set of multigroup equations for the first two moments of the electron distribution function: the balance equations for concentrations and electron flux densities. The hybrid model presented is intended to be used for numerical simulations of electric discharge processes in air proceeding with the participation of runaway electrons. The results of numerical simulations of electron transport in air in homogeneous and inhomogeneous electric fields, which were performed using the hybrid model, were compared with the corresponding results of simulations obtained using the Monte Carlo method. [ABSTRACT FROM AUTHOR]

Predictive models can improve the efficiency of wildlife management by guiding actions at the local, landscape and regional scales. In recent decades, a vast range of modelling techniques have been developed to predict species distributions and patterns of population spread. However, data limitations often constrain the precision and biological realism of models, which make them less useful for supporting decision‐making. Complex models can also be challenging to evaluate, and the results are often difficult to interpret for wildlife management practitioners. There is therefore a need to develop techniques that are appropriately robust, but also accessible to a range of end users. We developed a hybrid species distribution model that utilises commonly available presence‐only distribution data and minimal demographic information to predict the spread of roe deer (Capreolus caprelous) in Great Britain. We take a novel approach to representing the environment in the model by constraining the size of habitat patches to the home‐range area of an individual. Population dynamics are then simplified to a set of generic rules describing patch occupancy. The model is constructed and evaluated using data from a populated region (England and Scotland) and applied to predict regional‐scale patterns of spread in a novel region (Wales). It is used to forecast the relative timing of colonisation events and identify important areas for targeted surveillance and management. The study demonstrates the utility of presence‐only data for predicting the spread of animal species and describes a method of reducing model complexity while retaining important environmental detail and biological realism. Our modelling approach provides a much‐needed opportunity for users without specialist expertise in computer coding to leverage limited data and make robust, easily interpretable predictions of spread to inform proactive population management. [ABSTRACT FROM AUTHOR]

Waste management is an unavoidable technological operation in the process of raw material extraction. The main characteristic of this technological operation is the handling of large quantities of waste material, which can amount to several hundred million cubic metres. At the same time, this operation must comply with all administrative and environmental standards. Therefore, optimising waste rock management (particularly haulage and dumping) has the potential to significantly improve the overall value of the project. This paper presents a hybrid model for the optimisation of waste dump design and site selection. The model is based on different mathematical methods (Monte Carlo simulation, genetic algorithm, analytic hierarchy process and heuristic methods) adapted to different aspects of the problem. The main objective of the model is to provide a solution (in analytical and graphical form) for the draft waste dump design, on the basis of which the final waste dump design can be defined. The functioning of the model is verified using an example of an existing open pit. In the case study, 2250 members of the initial population (different waste dump variants) were generated, and a total of 110 optimised solutions were obtained using 15 optimisations. The solution with the best value of the objective function is adopted, and the final waste dump design is created. [ABSTRACT FROM AUTHOR]

The tetrafluoroethylene (TFE) polymerization process is an essential industrial process to produce polytetrafluoroethylene (PTFE), which is extensively utilized in aerospace and medical domains. A precise mechanism model is a prerequisite for comprehensively understanding this process. However, significant uncertainties in the kinetic model parameters may hinder attaining an optimal reaction rate. This study proposes a hybrid polymerization reaction model that integrates process mechanism modeling and data-driven modeling to address this challenge. In the hybrid modeling approach, the mechanism model for the polymerization reaction is developed based on reaction kinetic and thermodynamic assumptions. Additionally, a long short-term memory (LSTM) neural network is employed to predict the reaction rate for chain initiation by leveraging temporal relationships derived from archived measurements. The proposed methodology is implemented using a PTFE polymer reactor system, and experimental comparisons affirm its superior performance and effectiveness. [ABSTRACT FROM AUTHOR]

Simple Summary: The research aimed to address the challenges in the early diagnosis of Oral Squamous Cell Carcinoma (OSCC), a critical concern given its high fatality rate and global prevalence. Through the development of hybrid methodologies, the study sought to improve early diagnosis, reduce the burden on pathologists, and enhance the accuracy of OSCC diagnosis. By employing transfer learning, a combination of CNN and SVMs, and a fusion of deep and texture-based features, the research achieved a significant overall accuracy of 97.00%, effectively addressing the critical problem of timely and accurate OSCC diagnosis. Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient's chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively. [ABSTRACT FROM AUTHOR]

Drought simulation and prediction are of great significance to drought early warning. However, it is difficult to predict hydrological drought in data-scarce areas. To address this problem, Tarim River Basin was selected as a typical representative of the data-scarce inland river basin in China, we constructed a hybrid model by combining the complete ensemble empirical mode decomposition with adaptive noise and the long short-term memory method to predict hydrological drought from 2022 to 2100 based on CMIP6. The results show that meteorological drought has quasi-3-month, quasi-5-month, quasi-7-month, quasi-1-year, quasi-2-year, quasi-4-year, quasi-9-year, quasi-17-year and quasi-54-year cycles. Hydrological drought has quasi-3-month, quasi-5-month, quasi-6-month, quasi-1-year, quasi-2-year, quasi-4-year, quasi-9-year, quasi-29-year and quasi-32-year cycles. The components of meteorological drought and hydrological drought have significant correlations on monthly, interannual, and interdecadal scales, with correlation coefficients of 0.282, 0.573, and 0.340, respectively, and p values of 0.000. The hybrid model had a better prediction accuracy (R2 = 0.951, MAE = 0.131, NSE = 0.951, d index = 0.987) than previous studies. The trend of the hydrological drought index in the sustainable development model (SSP1-2.6) shows a trend of increasing severity with a rate of − 0.004/10 years from 2022 to 2100. And from the sustainable development model (SSP1-2.6) to the unbalanced development model (SSP5-8.5), the hydrological drought gradually becomes more serious. This study provides a new mechanism for predicting hydrological drought in data-scarce areas and is of great significance for the early warning of hydrological drought in this area. [ABSTRACT FROM AUTHOR]

The exponential growth of vehicles has led to increased traffic congestion and pollutants emission in cities. Intelligent transportation systems (ITS) shall be built by integrating Artificial Intelligence(AI). Planning optimal route with minimum travel time is a great challenge for the traffic management system. A hybrid model integrating long-short-term memory(LSTM) deep-learning model and Dijkstra's algorithm for recommending the fastest route is proposed in this work. The proposed hybrid approach reduces the travel time to a greater extent. Vehicle speed greatly influences eco-driving factors such as fuel consumption and exhaust emission. Thus, for each road links, the fuel consumption and emission of pollutants such as carbon monoxide(CO), nitrogen oxides(NOx), and hydrocarbon(HC) are estimated for different type of vehicles. The results showed the proposed approach decreases fuel consumption, CO emission, NOx emission, and HC emission up to 54.5%, 61.5%, 64.2%, and 81.9%, respectively, compared to the traditional shortest path. [ABSTRACT FROM AUTHOR]

Background: Pulmonary Tuberculosis is a major public health problem endangering people's health, a scientifically accurate predictive model is of great practical significance for the prevention and treatment of pulmonary tuberculosis. Methods: The reported incidence data of pulmonary tuberculosis were from the National Public Health Science Data Center (https://www.phsciencedata.cn/). The ARIMA, LSTM, EMD-SARIMA, EMD-LSTM, EMD-ARMA-LSTM models were established using the reported monthly incidence of tuberculosis reported in China from January 2008 to December 2018. The MSE, MAE, RMSE and MAPE were used to evaluate the performance of the models to determine the best model. Results: Comparing decomposition-based single model with undecomposed single model, it was found that: when predicting the incidence trend in the next year, compared with SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-SARIMA decreased by 39.3%, 19.0%, 22.1% and 19.8%, respectively. The MSE, MAE, RMSE and MAPE of EMD-LSTM were reduced by 40.5%, 12.8%, 22.9% and 12.7%, respectively, compared with the LSTM model; Comparing the decomposition-based hybrid model with the decomposition-based single model, it was found that: when predicting the incidence trend in the next year, compared with EMD-SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM model decreased by 21.7%, 10.6%, 11.5% and 11.2%, respectively. The MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM were reduced by 16.7%, 9.6%, 8.7% and 12.3%, respectively, compared with EMD-LSTM model. Furthermore, the performance of the model were consistent when predicting the incidence trend in the next 3 months, 6 months and 9 months. Conclusion: The prediction performance of the decomposition-based single model is better than that of the undecomposed single model, and the prediction performance of the combined model using the advantages of different models is better than that of the decomposition-based single model, so the EMD-ARMA-LSTM combination model can improve the prediction accuracy better than other models, which can provide a theoretical basis for predicting the epidemic trend of pulmonary tuberculosis and formulating prevention and control policies. [ABSTRACT FROM AUTHOR]

A multi-layer, deep-learning (DL) architecture consisting of stacked Convolutional Long Short Term Memory (sConvLSTM1D) layers is proposed to forecast the sunspot number (SSN) more effectively. The proposed model with optimized hyper-parameters performs efficiently on four kinds of sunspot data with different frequencies of time that are yearly, monthly, daily, and 13-month smoothed provided by the World Data Center-Sunspot Index and Long Term Solar Observation (WDC-SILSO), the Royal Observatory of Belgium (SILSO World Data Center). The model was contrasted with other traditional DL models on different performance metrics, namely root-mean-square error (RMSE), mean-absolute error (MAE), mean-absolute-percentage error (MAPE), and mean-absolute-scaled error (MASE). A non-parametric statistical test has also been carried out to confirm the model's effectiveness. The prediction of the highest yearly mean of total sunspot number (SSN) in Solar Cycle 25 (SC25) has also been performed. The proposed sConvLSTM1D model suggests that the solar cycle exhibits the characteristics of a weak cycle. However, it is anticipated to be stronger than the preceding Solar Cycle 24 (SC24). The year of peak sunspot number will be 2024, as per the prediction, with the peak value of yearly mean sunspot number as 140.84, which is 24.3% higher than the peak value of the yearly mean of total sunspot number, which was 113.3 in the Solar Cycle 24 in the year 2014. [ABSTRACT FROM AUTHOR]

Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a common and serious disease that has been studied by many researchers. Pima Indians Diabetes Dataset is one of the most famous datasets in this field. This study aims to increase the accuracy of machine learning algorithms in diagnosing the disease and to reveal the patterns that enable early diagnosis of the disease by focusing on the pre-processing stages. The proposed hybrid model includes "filling in missing values ​​with KNN", "examining six different normalization methods for normalization" and "removing outliers with K-means" in the pre-processing stage. In the data classification stage, four algorithms C4.5, SVM, Naïve Bayes and KNN were examined and the best hybrid model was found. The performance evaluation of these models is based on accuracy. The results were compared with previous studies and had higher accuracy of 98.3% and 99.1% for (KNN + n5 + K-means + SVM) and (KNN + n4/n3 + K-means + KNN), respectively. Finally, we offer the conclusive notes and some suggestions for further study. [ABSTRACT FROM AUTHOR]

Industrial multiphase flows are typically characterized by coexisting morphologies. Modern simulation methods are well established for dispersed [e.g., Euler-Euler (E-E)] or resolved [e.g., volume-of-fluid (VOF)] interfacial structures. Hence, a morphology adaptive multifield two-fluid model is proposed that is able to handle dispersed and resolved interfacial structures coexisting in the computational domain with the same set of equations. An interfacial drag formulation for large interfacial structures is used to describe them in a VOF-like manner. For the dispersed structures, the baseline model developed at Helmholtz-Zentrum Dresden-Rossendorf is applied. The functionality of the framework is demonstrated by investigating a single rising gas bubble in a stagnant water column, a two-dimensional stagnant stratification of water and oil sharing a large-scale interface that is penetrated by micro gas bubbles, and an isothermal countercurrent stratified flow case. Recent developments focus on the transition region, where bubbles are overresolved or underresolved either for E-E or for VOF. Furthermore, a concept is presented for the transition of oversized dispersed bubbles into the resolved phase. [ABSTRACT FROM AUTHOR]

Recently, the dynamic distribution of resources and task scheduling has played a critical role in cloud computing to achieve maximum storage and performance. The allocation of computational tasks in the cloud is a complicated process that can be affected by some factors, such as available network bandwidth, makespan, and cost considerations. However, these allocations are always non-symmetric. Therefore, it is crucial to optimize available bandwidth for efficient cloud computing task scheduling. In this research, a novel swarm-based task scheduling with a security approach is proposed to optimize the distribution of tasks using available resources and encode cloud information during task scheduling. It can combine the Moth Swarm Algorithm (MSA) with the Chameleon Swarm Algorithm (CSA) for the task scheduling process and utilizes the Polymorphic Advanced Encryption Standard (P-AES) for information security of cloud scheduled tasks. The approach offers a new perspective for utilizing swarm intelligence algorithms to optimize cloud task scheduling. The integration of MSA and CSA with P-AES enables the approach to provide efficient and secure task scheduling by exploiting the strengths of used algorithms. The study evaluates the performance of the proposed approach in terms of the degree of imbalance, makespan, resource utilization, cost, average waiting time, response time, throughput, latency, execution time, speed, and bandwidth utilization. The simulation is carried out using a wide range of tasks from 1000 to 5000. The results show that the approach provides an innovative solution to the challenges of task scheduling in cloud environments and improves the performance of cloud services in terms of effectiveness and security measures. [ABSTRACT FROM AUTHOR]