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This work introduces a quasi-3D refined theory for assessing the transient dynamic reactions of a sandwich annular sector plate subjected to mechanical shock loading. The sandwich structure has two facesheets composed of graphene origami (GOri)-enabled auxetic metal metamaterials (GOEAMs) and a core composed of carbon fiber reinforced polymer. The auxetic quality of the annular plates is mainly determined by the quantity of graphene and the level of folding in the GOri material. The elements are assessed layer by layer across the thickness of the plates. Micromechanical models supported by genetic programming may be used to predict the position-dependent Poisson's ratio and other material parameters. The concept of Hamiltonian is used to deduce the governing equations of the structure. The equations of motion, which vary with time, are solved using the numerical solution process and the Laplace transform. A comprehensive parametric study is carried out to examine the influence of various geometric and physical parameters on the time-dependent behavior of annular sector plates. The current mathematical modeling results are being compared to the findings of previous works, as well as a machine learning technique. By using this machine learning methodology, it is feasible to computationally solve differential equations at a reduced expense, while simultaneously surmounting the challenge of formulation. To use machine learning techniques, a dependable dataset acquired from either experimental or numerical analysis is necessary. The dataset was created using the quantitative findings of the investigation. Furthermore, the machine learning technique demonstrates its capacity to provide very precise outcomes when predicting the transient behavior of the existing structure under novel loading and boundary circumstances. [ABSTRACT FROM AUTHOR]

This work introduces the novel analysis of the bending behavior of a composite doubly curved panel made of graphene origami (GOri)-enabled auxetic metal metamaterials (GOEAMs) subjected to external mechanical force. The motivation for this study stems from the many applications of such structures in the field of aeronautics. These materials have mechanical and structural properties that continuously vary. This study is the first to examine the effects of an instantaneous external shock on the bending response of a composite doubly curved panel made of GOEAMs due to the extraordinary properties of these advanced materials. The mathematical governing equations are obtained from the higher-order shear deformation theory and are solved using the Laplace transform method (LTM) and the analytical solution procedure (ASP). After solving the equations, the results of the current system are compared to those of a previously published work, revealing a significant level of concurrence between the two sets of data. This study presents a machine learning approach that utilizes a deep neural network (DNN) with input, hidden, and output layers, as well as independent variables and other relevant factors. The aim is to provide an efficient computational technique for solving engineering issues. This is achieved by the use of mathematical modeling and the verification of the current output's outcomes. Additionally, a database is supplied for an in-depth examination of this structure's suitability for aeronautical purposes. The database contains comprehensive bending information, including normal, shear, and displacement fields in various directions, specifically for the GOEAMs subjected to external shock loading. [ABSTRACT FROM AUTHOR]

Over the last several decades, there has been a lot of research interest in the micro/nano-scale energy harvester, a technology that offers sustainable energy solutions for different micro/nano-electromechanical systems. Investigate various geometries, topologies, and material options for energy receiver designs, as well as interesting nonlinear responses that may be used to push the boundaries of energy receiver design, energy efficiency, and energy density. In the current work, the residual stresses in the system after fabrication, are modeled as the in-plane loading in the system. Although the majority of designs are restricted to MEMS alone, research shows that in-plane loading in the system greatly affects the non-linear vibration of the energy collector and illustrates the potential of increasing its performance by controlling the in-plane load. The impacts of in-plane loading in the MEMS at the microscale tissues were quantified by creating a stress-based piezoelectric-flexoelectric MEMS model and analyzing the non-linear frequency response using a time integrator and a Newtonian iterator. By contrasting them with the outcomes of mathematically modeling the current system and contrasting the outcomes of the current technique with the outcomes of the earlier study, the findings in a Python environment known as the XGBoost methodology are re-validated. This strategy is founded on the concept of "reinforcement," which integrates many training methodologies with the detection of underperforming individuals to cultivate proficient learners. Finally, numerous suggestions for raising the nonlinear vibration of the energy harvester are carefully considered. [ABSTRACT FROM AUTHOR]

For the first time, in the current work, the quasi-3D new refined theory (Q3D-NRT) is used to analyze the dynamic reactions of functionally graded (FG) annular sector plates made of graphene origami (GOri)-enabled auxetic metal metamaterials to the mechanical shock loading. The auxetic property of the annular plates is effectively controlled by graphene content and GOri folding degree that is graded across the thickness direction of the annular plates in a layer-wise manner, as Poisson's ratio and other material properties are position-dependent and can be estimated by genetic programming-assisted micromechanical models (MM). Hamilton's concept is used to find the structure's governing equations. The differential quadrature technique and the Laplace transform are used at design sites to solve the time-varying equations of motion. The system response is translated from the Laplace domain to the time domain using a modified version of the Dubner and Abate approach. To investigate the impact of different geometrical and physical factors on the dynamic reactions of the annular sector plates, thorough parametric research is conducted. The findings of the present mathematical modeling are compared with those of the earlier publications and also with those of the machine learning approach. Utilizing this learning strategy, differential equations may be solved with very cheap computer costs while also overcoming formulation complexity. It needs a valid dataset from experimental or numerical analysis to use machine learning techniques. This dataset was compiled using the study's numerical findings. Additionally, the machine learning approach demonstrates the capacity to deliver findings with a high degree of accuracy when predicting the mechanical characteristics of the existing structure under novel loading and boundary circumstances. [ABSTRACT FROM AUTHOR]

To improve radar-based quantitative precipitation estimation (QPE) methods, this study investigated the relationship between radar reflectivity (Z) and hourly rainfall intensity (R) using data from 289 precipitation events in Shanghai between September 2020 and March 2024. Two Z-R relationship models were compared in terms of their fitting performance: Z = 270.81 R1.09 (empirically fitted relationship) and Z = 300 R1.4 (standard relationship). The results show that the Z = 270.81 R1.09 model outperforms the Z = 300 R1.4 model in terms of fitting accuracy. Specifically, the Z = 270.81 R1.09 model more effectively captures the nonlinear relationship between radar reflectivity and rainfall intensity, with a higher degree of agreement between the fitted curve and the observed data points. This model demonstrated superior performance across all 289 precipitation events. This study evaluated the performance of four machine learning approaches while incorporating five meteorological features: specific differential phase shift (KDP), echo-top height (ET), vertical liquid water content (VIL), differential reflectivity (ZDR), and correlation coefficient (CC). Nine QPE models were constructed using these inputs. The key findings are as follows: (1) For models with a single-variable input, the KAN deep learning model outperformed Random Forest, Gradient Boosting Decision Trees, Support Vector Machines, and the traditional Z-R relationship. (2) When six features were used as inputs, the accuracy of the machine learning models improved significantly, with the KAN deep learning model outperforming other machine learning methods. Compared to using only radar reflectivity, the KAN deep learning model reduced the MRE by 20.78%, MAE by 4.07%, and RMSE by 12.74%, while increasing the coefficient of determination (R2) by 18.74%. (3) The integration of multiple meteorological features and machine learning optimization significantly enhanced QPE accuracy, with the KAN deep learning model performing best under varying meteorological conditions. This approach offers a promising method for improving radar-based QPE, particularly considering seasonal, weather system, and precipitation stage differentiation. [ABSTRACT FROM AUTHOR]

Context: Advanced copper and copper alloys, as significant engineering structural materials, have recently been extensively used in energy, electron, transportation, and aviation domains. Higher requirements urge the emergence of high-performance copper alloys. However, the traditional trial-and-error experimental observations and computational simulation research used to design and develop novel materials are time-consuming and costly. With the accumulation of material research and rapid development of computational ability, the thorough application of material genome engineering has sped up the development of novel materials and facilitates the process of systematic engineering application. Methods: This review summarizes the benefits of data-driven machine learning techniques and the state of the art of machine learning research in the area of copper alloys. It also displays the widely used computational simulation approaches (e.g., the first-principles calculation, molecular dynamics simulation, phase-field simulations, and finite element analysis) and their combined applications in material design and property prediction. Finally, the limitations of machine learning research methods are outlined, and future development directions are proposed. [ABSTRACT FROM AUTHOR]

AbstractThe major free spans of submarine pipelines, caused by the combined action of waves and currents can pose significant risks to pipeline operational safety. In this study, a numerical model based on the Reynolds-averaged Navier–Stokes (RANS) equation and sediment transport principles was established to investigate local scouring around a pipeline under wave and current conditions. The influence of the pipeline diameter D, flow incident angle a, current velocity Uc and KC number on the scour depth was analyzed. Based on a total of 145 sets of numerical simulation cases, a dataset was established, and all data were normalized and divided into a training set (80%) and a test set (20%). A decision-tree regression model was used to train the dataset, and a machine-learning prediction model was constructed. In the testing stage, the model had R2 = 0.94, RMSE = 0.043 for the scour depth S prediction; R2 = 0.92, RMSE = 0.096 for the dimensionless scour depth S/D prediction; and R2 = 0.97, RMSE = 0.267 for the scour hole width L prediction. The machine-learning prediction model exhibited good performance and computational efficiency, and can be used for rapid prediction of submarine pipeline scour characteristics. [ABSTRACT FROM AUTHOR]

Soil erosion has always been a critical issue confronting watershed environments, impacting the progress of sustainable development. As an increasing number of countries turn their attention to this problem, numerous policies have been enacted to halt the progression of soil erosion. However, policy-driven interventions often lead to significant changes in watershed vegetation coverage, under which circumstances, the original sediment erosion models may fall short in terms of simulation accuracy. Taking the Kuye River watershed as the research subject, this study investigates soil erosion data spanning from 1981 to 2015 and utilizes the Revised Universal Soil Loss Equation (RUSLE) model to simulate soil erosion. It is found that the extensive planting of vegetation after 2000 has led to a rapid reduction in soil erosion within the Kuye River watershed. The original vegetation cover and management factor (C) proves inadequate in predicting the abrupt changes in vegetation coverage. Consequently, this study adopts two improved plant cover and management factor equations. We propose two new methods for calculating the vegetation cover and management factor, one using machine learning techniques and the other employing a segmented calculation approach. The machine learning approach utilizes the Eureqa software (version11.0, Cornell University, New York, American) to search for the relationship between Normalized Difference Vegetation Index (NDVI) and C, ultimately establishing an equation that describes this relationship. On the other hand, the piecewise method determines critical values based on data trends and provides separate formulas for C above and below these critical values. Both methods have achieved superior calculation accuracy. Specifically, the overall data calculation using the machine learning method achieved an determined coefficient (R2) of 0.5959, while the segmented calculation method achieved an R2 of 0.6649. Compared to the R2 calculated by the traditional RULSE method, these two new methods can more accurately predict soil erosion. The findings of this study can provide valuable theoretical reference for water and soil prediction in watersheds. [ABSTRACT FROM AUTHOR]

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The carbon emissions of urban residential buildings are substantial. However, the standard operating conditions specified in current energy-saving standards significantly differ from the actual energy consumption under real operating conditions. Therefore, it is essential to consider the impact of residents' actual energy consumption behavior in carbon emission forecasts. To improve the accuracy of carbon emission predictions for urban residential buildings, this paper focuses on residential buildings in Guangzhou. Taking into account the energy consumption behavior of residents, parameterized modeling is carried out in the R language, and simulation is carried out using EnergyPlus software. Analysis revealed that the higher the comfort level of residential energy consumption behavior, the more it is necessary to encourage residents to adopt energy-saving behaviors. Combining carbon emission factors, air-conditioning energy efficiency, and the power consumption models of lighting and electrical equipment, a comprehensive operational carbon emission prediction model for urban residential operations in Guangzhou was developed. By comparing the prediction model with an actual case, it was found that the prediction deviation was only 4%, indicating high accuracy. The proposed operational carbon emission model can quickly assist designers in evaluating the carbon emissions of urban residential buildings in the early stages of design, providing an accurate basis for decision-making. [ABSTRACT FROM AUTHOR]

The creation of effective systems for filtering media texts is due to the need to develop artificial intelligence systems, which is a large language model that should be trained using “correct” text samples that do not contain signs of disinformation, infodemic and unreliability. The article presents the results of automatic detection of high-quality media texts, as well as text samples with infodemic features carried out using a trained natural language model based on a manually labeled corpus. Manual marking of the corpus was carried out by experts based on the parameterization of the text content. The goal of our work is to build a model of the language of media messages, assess the quality and identify detection errors caused by the linguistic characteristics of texts. Creating a model of the language of media messages is a condition for increasing the efficiency and quality of artificial intelligence systems. It has been established that the test use of a trained natural language model allows filtering media texts with fairly high accuracy. The support vector machine method proved to be most effective. The share of incorrectly recognized informative texts that meet the criteria of reliability and novelty is low and amounts to 6.2 percent. The percentage of incorrectly recognized uninformative texts is approximately 3.9 percent, which indicates a fairly high efficiency of the developed model. The errors in the detection of informative texts are associated with the use of proper names (anthroponyms, toponyms) and numerals in the headings. Linguistic features of misclassified texts containing signs of fake and misinformation comprise text samples using statements with speech verbs that are often used in informative texts.

【Objective】 The leaf area index (LAI) is an important trait of plant canopies but challenging to measure accurately at large scales. We studied the feasibility of using multispectral imaging and machine learning to estimate the LAI of spring wheat. 【Method】 The experiment was conducted in a spring wheat field on the Tumochuan Plain in the Yellow River Basin, Inner Mongolia. Images of the spring wheat at the jointing, booting, and grain-filling stages were acquired using a multispectral camera mounted on a DJI P4M UAV. Selected vegetation indices were subjected to principal component analysis (PCA), and the resulting components were used to estimate LAI. We compared six models: multiple linear regression (MLR), decision tree regression (DTR), backpropagation neural network regression (BPNN), gradient boosting decision tree regression (GBDT), support vector machine regression (SVR), and random forest regression (RFR). LAI was calculated separately for each growth stage using different vegetation indices. 【Result】 LAI was significantly correlated with the normalized difference vegetation index (NDVI), modified simple ratio (MSR), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and normalized difference red edge index (NDRE). It showed a weak correlation with the renormalized difference vegetation index (RDVI) during the heading and grain-filling stages, with their correlation coefficients being 0.23 and 0.21, respectively. The BPNN model was most accurate during the jointing stage, with R2, RMSE, and MAE being 0.822, 0.305, and 0.257, respectively. In contrast, the RFR model performed best during the heading, grain-filling, and entire growth periods, with R2 being 0.613, 0.811 and 0.834, RMSE being 0.189, 0.150 and 0.174, and MAE being 0.126, 0.121 and 0.133, respectively. Additionally, the RFR model constructed using data from all three stages was more accurate than models derived from data at individual growth stages. 【Conclusion】 Multispectral data acquired via UAV, combined with machine learning algorithms, can accurately estimate the LAI of spring wheat at various growth stages. Models constructed using data from multiple growth stages are more accurate than those based on a single stage. The models are most accurate for the booting stage and least for the heading stage. Overall, the RFR model provided the most accurate LAI estimates across the three growth stages.

The development of bullying victimization among adolescents displays significant individual variability, with general, group-based interventions often proving insufficient for partial victims. This study aimed to conduct a machine learning-based predictive analysis of bullying victimization trajectories among Chinese early adolescents and to examine the underlying determinants. Data were collected from 1549 students who completed three assessments of bullying victimization from 2019 to 2021. Self-reported questionnaires were used to measure bullying victimization and its associated risk and protective factors. Trajectories were classified using the Group-based Trajectory Model (GBTM), while a Random Forest algorithm was employed to develop a predictive model. Associations between baseline characteristics and victimization trajectories were evaluated via multiple logistic regression analysis. The GBTM identified four distinct victimization trajectories, with the predictive model demonstrating adequate accuracy across these trajectories, ranging from 0.812 to 0.990. Predictors exhibited varying influences across different trajectory subgroups. Odds ratios (ORs) were notably higher in the persistent severe victimization group compared to the low victimization group (OR for adverse school experiences: 3.698 vs. 1.386; for age: 2.160 vs. 1.252; for irritability traits: 1.867 vs. 1.270). Adolescents reporting lower school satisfaction and higher borderline personality features showed a greater likelihood of persistent severe victimization, while those with lower peer satisfaction faced increased victimization over time. The machine learning-based predictive model facilitates the identification of adolescents across different victimization trajectory groups, offering insights for designing targeted interventions. The identified risk factors are instrumental in guiding effective intervention strategies. • The machine learning model showed a desirable performance in the prediction of bullying victimization trajectories. • The important predictors presented different effect across different trajectory subgroups. • Physical aggression and hostility were found significantly associated with low victimization trajectory. • Satisfaction with school and borderline personality features were associated with persistent severe victimization. • The results might provide valuable insights in identifying at-risk groups and designing targeted intervention strategies. [ABSTRACT FROM AUTHOR]

Aim. To identify significant indicators of cognitive dysfunction based on discriminant analysis and to assess the influence of the course, nature and localization of ischemic stroke on the cognitive status of the patient.Materials and methods. We examined 290 patients diagnosed with ischemic stroke in the carotid artery area. Depending on presence of cognitive dysfunction according to the Montreal Cognitive Assessment Scale (MoSA) patients were divided into 2 groups: 240 patients with cognitive decline (≤25 point by MoCA) and 50 patients without it. In order to verify the markers, anamnestic characteristics were assessed, cognitive-functional indicators (according to the scales of the National Institutes of Health, MoCA, Bartel, Rankin, IQCODE questionnaire, additional scales to assess praxis, semantic aphasia, perception and executive function), data of neuroimaging studies. For statistical analysis machine learning algorithms and Python with its libraries (Pandas and SciPy) were implied.Results. The main neuropsychological indicators for patients with early post-stroke cognitive impairment were decline in the areas of perception, executive function, memory and semantic information processing, affective disturbances and physical fatigue. Relevant indicators identified during estimation of the instrumental and clinical examination results were severity of IS, left frontal and right parietal localisations of ischemia focus, presence of cortical atrophy and leukoaraiosis.Conclusion. Based on multi-factor analysis of clinical and paraclinical parameters using machine learning algorithms, the main markers of cognitive decline of early post-stroke impairments were identified. This will allow us to optimise the choice of neurocognitive rehabilitation strategies and to personalise the approach in the further management of the stroke patient.

This review article details the advancements in detecting heavy metals in aquatic environments using remote sensing methodologies. Heavy metals are significant pollutants in aquatic environment, and their detection and monitoring are crucial for predicting water quality. Traditional in situ water sampling methods are time-consuming and costly, highlighting the advantages of remote sensing techniques. Analysis of the reflectance and absorption characteristics of heavy metals has identified the red and near-infrared bands as the sensitive wavelengths for heavy metal detection in aquatic environments. Several studies have demonstrated a correlation between total suspended matter and heavy metals, which forms the basis for retrieving heavy metal content from TSM data. Recent developments in hyperspectral remote sensing and machine (deep) learning technologies may pave the way for developing more effective heavy metal detection algorithms. [ABSTRACT FROM AUTHOR]

Round-ended concrete-filled steel tubes (RECFSTs) present very different performances between the primary and secondary axes, which renders them particularly suitable for use as bridge piers and arches. In recent years, research into RECFST heavily relies on experimental procedures restricting the parameter range under consideration, which narrows the far-reaching applicability of RECFST. This study employs advanced machine learning methods to predict the axial load-bearing capacity of RECFST with a wide parameter range. Firstly, a machine learning database comprising 2400 RECFSTs is established, which covers a wider range of commonly used material strengths and cross-sectional dimensions. Three machine learning prediction models of this database are then developed, respectively, using different algorithms. The robustness of the machine learning models is evaluated by predicting the axial load-bearing capacity of 60 RECFST specimens from existing references. The results demonstrated that the machine learning models provided superior predictive accuracy compared to theoretical or code-based formulas. A graphical user interface (GUI) is ultimately developed based on the machine learning prediction models to predict the axial load-bearing capacity of RECFST. This tool facilitates rapid and accurate RECFST design. [ABSTRACT FROM AUTHOR]

This paper investigated a new speed regulator using an adaptive transitioning policy iteration learning technique for the variable reluctance motor (VRM) drive. A transitioning strategy is used in this unique scheme to handle the nonlinear behavior of the VRM by using a series of learning centers, each of which is an individual local learning controller at linear operational location that grows throughout the system's nonlinear domain. This improved control technique based on an adaptive dynamic programming algorithm is developed to derive the prime solution of the infinite horizon linear quadratic tracker (LQT) issue for an unidentified dynamical configuration with a VRM drive. By formulating a policy iteration algorithm for VRM applications, the speed of the motor shows inside the machine model, and therefore the local centers are directly affected by the speed. Hence, when the speed of the rotor changes, the parameters of the local centers grid would be updated and tuned. Additionally, a multivariate transition algorithm has been adopted to provide a seamless transition between the Q-centers. Finally, simulation and experimental results are presented to confirm the suggested control scheme's efficacy. [ABSTRACT FROM AUTHOR]

In this study, taking the Jurassic Lianggaoshan Formation (J1l) tight sandstones in the eastern Sichuan Basin as an example, the types and well-logging responses of main sedimentological and diagenetic facies in the lacustrine delta-front are investigated based on summarizing the sedimentary characteristics and reservoir properties. Subsequently, further validation and application are conducted in the study area through machine learning. Research results show that the J1l lacustrine delta-front in the eastern Sichuan Basin mainly develops subaqueous distributary channels and mouth bar sand bodies, exhibiting typical densification reservoirs, with porosity and permeability distributed between 0.48% and 11.24% (av. 3.87%) and 0.0003-0.653 x 10-3 µm² (av. 0.026 x 10-3 µm²), respectively. Strong compaction and strong cementation are the primary factors leading to densification, whereas chlorite coatings and weak dissolution play constructive roles in preserving some primary pores, creating a small amount of dissolution pores, and enhancing permeability. In terms of manifestation, the pore-throat content with a radius greater than 0.006 µm governs the reservoir quality. Furthermore, five types of diagenetic facies are identified in the J1l subaqueous distributary channels and mouth bars: strong compaction facies (Type I), strong cementation facies (Type II), chlorite-coating and intergranular pore facies (Type III), weak dissolution and intragranular pore facies (Type IV), and medium compaction and cementation facies (Type V). Overall, the thick and coarse-grained subaqueous distributary channels can be considered as the preferred exploration targets for tight oil and gas, with type III and type IV diagenetic facies being the most favorable reservoirs, characterized by well-logging responses of high AC and low GR, DEN, and RT. Based on the fine division of sedimentological and diagenetic facies, establishing well-logging interpretation models and then employing machine learning to achieve sweet spot reservoir prediction can provide valuable insights for tight oil and gas exploration in regions lacking core data. [ABSTRACT FROM AUTHOR]

Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building's operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation. [ABSTRACT FROM AUTHOR]

In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton's tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. To achieve this, we gathered data on BTK inhibitors from the Reaxys and ChEMBL databases, removing compounds with covalent bonds and duplicates to obtain a dataset of 3895 inhibitors of non-covalent. These inhibitors were characterized using MACCS fingerprints and Morgan fingerprints, and four traditional machine learning algorithms (decision trees (DT), random forests (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost)) were used to build 16 classification models. In addition, four deep learning models were developed using deep neural networks (DNN). The best model, Model D_4, which was built using XGBoost and MACCS fingerprints, achieved an accuracy of 94.1% and a Matthews correlation coefficient (MCC) of 0.75 on the test set. To provide interpretable explanations, we employed the SHAP method to decompose the predicted values into the contributions of each feature. We also used K-means dimensionality reduction and hierarchical clustering to visualize the clustering effects of molecular structures of the inhibitors. The results of this study were validated using crystal structures, and we found that the interaction between the BTK amino acid residue and the important features of clustered scaffold was consistent with the known properties of the complex crystal structures. Overall, our models demonstrated high predictive ability and a qualitative model can be converted to a quantitative model to some extent by SHAP, making them valuable for guiding the design of new BTK inhibitors with desired activity. [ABSTRACT FROM AUTHOR]

The focus of this study is to apply finite element method (FEM) and machine learning methods to investigate the interfacial bonding strength of continuous fiber reinforced thermoset composite (TSC)‐thermoplastic structures manufactured through co‐curing and overmolding processes, with polyamide 6 (PA 6) as the thermoplastic material. A model for interfacial healing degree in TSC‐PA 6 structures was developed. Then, FEM was employed to study the influence of various injection overmolding process parameters on the interfacial bonding strength of TSC‐PA 6 structures. The results show that there is a strong correlation between the degree of interface healing and the bonding strength. Subsequently, six machine learning methods were employed to correlate interfacial healing degree with diverse injection molding process parameters. Simulation data were utilized for training, calibration, and validation of the six machine learning models. Based on the results of simulation and machine learning predictions, a quantitative analysis of the significance of injection molding process parameters on healing degree was conducted. These parameters are ranked in descending order of importance as follows: insert temperature, melt temperature, and injection rate. Highlights: The interfacial healing degree model of PA 6 was established and validated through the integration FEM and experiment.Six machine learning models were built to predict the interfacial healing degree.Grad boosting performed best in predicting the interfacial healing degree.Insert temperature had the greatest impact on the interface healing degree. [ABSTRACT FROM AUTHOR]

It is crucial to clarify the nonlinear effects of urban multidimensional characteristics on land surface temperature (LST). However, the combined consideration of the urban green space (UGS), water bodies, buildings, and socio-economic factors is limited. And the diurnal differences in their thermal effects have been less considered. In this study, central Beijing was taken as study area. Local climate zones (LCZ) were firstly applied to reveal spatiotemporal heterogeneity of LST. Then, the interpretable machine learning methods were utilized to quantitatively reveal nonlinear thermal effects of urban multidimensional characteristics, i.e., the UGS, water bodies, and building landscape features, and socio-economic features. The results indicated that built type LCZs have a higher average LST compared to natural type LCZs. And the LST of built type LCZs is simultaneously influenced by buildings’ density and height characteristics. Daytime LST is mainly affected by the landscape proportions of UGS, buildings, and trees, while nighttime LST is more influenced by socio-economic and building characteristics. The thermal effects of key factors exhibit nonlinear characteristics. Whether during the day or night, the impact of building coverage on LST is greater than that of building height, consistently exhibiting a warming effect. While, the building height and water body edge density factors both exhibited a reversal trend in their thermal impact between day and night. Our study also emphasized the importance of trees type in UGS and provided recommendations for UGS planning based on sensitivity and contribution considerations. These findings can help to regulate urban LST and promote sustainable urban development.

Objectives. Currently, the main source of information is the Internet. The huge amount of information available on the Internet makes it urgent to comprehensively analyze data from open Internet sources.The goal of this work is to create a multi-purpose, modifiable cluster for in-depth analysis of data from Internet sources, the main objectives of which are to identify the most important publications in a certain subject area, thematic analysis of these publications, identifying the leader of a scientific direction and determining trends in the development of areas and interaction of groups of people.Methods. To solve this problem, a methodology was developed for constructing a multi-purpose cluster using technologies for quickly constructing a thematic graph database, a knowledge graph, methods and models of machine learning for in-depth analysis of data.Results. A system for comprehensive analysis of data from thematic sites ISKAD IS has been developed, a methodology for quickly constructing a thematic graph database and a comprehensive technology for in-depth analysis of data from Internet sources and analysis of data from the most important well-known world sites have been tested.Conclusion. An IT environment has been created for the rapid construction of thematic graph databases. The results of using the technology for quickly constructing graph databases are shown using examples of the work of ISKAD IS.

The carbon emissions of urban residential buildings are substantial. However, the standard operating conditions specified in current energy-saving standards significantly differ from the actual energy consumption under real operating conditions. Therefore, it is essential to consider the impact of residents’ actual energy consumption behavior in carbon emission forecasts. To improve the accuracy of carbon emission predictions for urban residential buildings, this paper focuses on residential buildings in Guangzhou. Taking into account the energy consumption behavior of residents, parameterized modeling is carried out in the R language, and simulation is carried out using EnergyPlus software. Analysis revealed that the higher the comfort level of residential energy consumption behavior, the more it is necessary to encourage residents to adopt energy-saving behaviors. Combining carbon emission factors, air-conditioning energy efficiency, and the power consumption models of lighting and electrical equipment, a comprehensive operational carbon emission prediction model for urban residential operations in Guangzhou was developed. By comparing the prediction model with an actual case, it was found that the prediction deviation was only 4%, indicating high accuracy. The proposed operational carbon emission model can quickly assist designers in evaluating the carbon emissions of urban residential buildings in the early stages of design, providing an accurate basis for decision-making.

Public investments in subway systems are often motivated by improving local air quality. Recent studies, however, have reached different conclusions on the air quality benefits of subway investment. To reconcile these findings, this paper examines the air quality effects of all 359 subway line openings in China between 2013 and 2018. The machine learning method adopted in this paper substantially improves the consistency and precision of the estimates by purging seasonality, volatility, and the nonlinear effects of meteorological conditions in air quality data. The empirical results suggest an insignificant short-term effect and a significant long-term effect, which is expected as the adjustment of commuting mode takes time. Using the causal forest approach, the heterogeneity analysis find that a city that is experiencing rapid economic growth from a lower income level and currently has fewer subway lines is more likely to experience statistically significant improvements in air quality from a subway opening. These findings help reconcile the different findings in the literature and shed light on air pollution reduction as one of the objectives of public transit investment. [ABSTRACT FROM AUTHOR]

Symmetric Aerostatic Cavities Bearing (SACB) systems have attracted increasing attention in the field of high-precision machinery, particularly rotational mechanisms applied at ultra-high speeds. In an air bearing system, the air bearing serves as the main support, and the load-carrying capacity is not as high as that of oil film bearings. However, the aero-spindle can operate at considerably high rotational speeds with relatively lower heat generated from rotation compared with that of oil film bearings. In addition, the operating environment of air bearings does not easily cause the rotor to deform. Hence, through adequate design, air pressure systems exhibit a certain level of stability. In general, the pressure distribution function of air bearings exhibits strong nonlinearity when there are changes in the rotor mass or rotational speed, or when the bearing system is inadequately designed. These issues may lead to instabilities in the rotor, such as unpredictable nonperiodic movements, rotor collisions, or even chaotic movements under certain parameters. In this study, rotor oscillation was analyzed using the maximum Lyapunov exponent to identify whether chaotic behavior occurred. Machine learning methods were then used to establish models and predict the rotor behavior. Especially, random forest and extreme gradient boosting were combined to develop a new model and confirm whether this model offered higher prediction performance and more accurate results in predicting tendencies with considerable changes compared with other models. The results can be effectively used to predict the SACB system and prevent nonlinear behavior from occurring. [ABSTRACT FROM AUTHOR]

Forecasting the electricity-to-water conversion coefficient (EWCC) can help manage and plan irrigation water in arid and semiarid areas. However, the EWCC is influenced by several factors, making it difficult to develop an analytical model for validation or prediction. Therefore, this study selected 206 typical irrigation wells in Baicheng City to conduct EWCC tests in a field investigation to gather information regarding the results and related influencing factors. Subsequently, machine learning models (multiple linear regression model, support vector model, and backpropagation neural network) were trained, validated, and tested, and their precisions were evaluated and compared. The backpropagation neural network model was the most accurate, followed by the support vector and multiple linear regression models. The backpropagation neural network model results were consistent with those of the field survey, and this model was thus used to forecast the EWCC for all the townships in Baicheng City. The forecasting models revealed that most towns had an EWCC from 3 to 7 m3/kW·h, with an EWCC greater than 7 observed in the Tao'er River Fan and Yueliangpao District. The BP models developed in this study proved to be dependable and applicable for forecasting the EWCC in this area. [ABSTRACT FROM AUTHOR]

ObjectiveBased on machine learning method, four types of early postoperative frailty risk prediction model of enterostomy patients were constructed to compare the performance of each model and provide the basis for preventing early postoperative frailty of elderly patients with enterostomy.MethodsThe prospective convenience sampling method was conducted and 362 early postoperative enterostomy patients were selected in three hospitals from July 2020 to November 2023 in Shanghai, four different prediction models of Support Vector Machine (SVM), Bayes, XG Boost, and Logistic regression were used and compared the test effects of the four models (MCC, F1, AUC, and Brier index) to judge the classification performance of the four models in the data of this study.ResultsA total of 21 variables were included in this study, and the predictors mainly covered demographic information, stoma-related information, quality of life, anxiety and depression, and frailty. The validated models on the test set are XGBoost, Logistic regression, SVM prediction model, and Bayes on the MCC and F1 scores; on the AUC, XGBoost, Logistic regression, Bayes, and SVM prediction model; on the Brier scores, Bayes, Logistic regression, and XGBoost.ConclusionXGBoost based on machine learning method is better than SVM prediction model, Logistic regression model and Bayes in sensitivity and accuracy. Quality of life in the early postoperative period can help guide clinical patients to identify patients at high risk of frailty and reduce the incidence of early postoperative frailty in elderly patients with enterostomy.

Accurate forecasting of the treasury bond market is beneficial for financial institutions to formulate investment research strategies and for national managers to build a modern financial system. This paper integrates the ideas of improved multivariate time series sampling and deep learning prediction model structure optimization, and proposes an optimized deep learning model framework under the LASSO-SMLR-PCA machine learning method. Through the LASSO and SMLR methods, the multicollinearity of the multivariate time series is reduced and the variables with insignificant correlation coefficients are eliminated. Then, the PCA method is used for dimensionality reduction and reconstruction, and finally, the LSTM deep learning model with Bayesian optimized hyperparameters is used to achieve rolling time prediction of the treasury bond market yield price. The empirical results show that the optimized deep learning model performs excellently in terms of evaluation indicators for treasury bond yield price forecasting, with accurate curve fitting, efficient model structure, and stable and effective practical application.

Engagement is an essential component of the learning processes associated with positive learning outcomes. Measuring learner engagement in learning processes is vital to providing insights for enhancing learning activities. Because the learning paradigm has shifted to enable more distance learning practices, machine learning-based automatic engagement estimation methods have been proposed as a new way to measure learner engagement. Nevertheless, most existing methods are built standalone and have yet to be integrated into actual distance learning practice. Furthermore, implementing automatic engagement estimation should ensure technological and ethical impact responsibilities. This article proposes a design principle for the end-to-end integration of automatic engagement in distance learning practice. The MeetmEE system design was introduced to measure learners’ emotional engagement in a synchronous distance learning practice. The MeetmEE prototype was deployed in a pilot experiment to evaluate the MeetmEE system design. Finally, the user evaluation results are considered to construct the design principle of ethical implementation. The design principle for implementing the automatic engagement estimation incorporates technical and operational measures.

Round-ended concrete-filled steel tubes (RECFSTs) present very different performances between the primary and secondary axes, which renders them particularly suitable for use as bridge piers and arches. In recent years, research into RECFST heavily relies on experimental procedures restricting the parameter range under consideration, which narrows the far-reaching applicability of RECFST. This study employs advanced machine learning methods to predict the axial load-bearing capacity of RECFST with a wide parameter range. Firstly, a machine learning database comprising 2400 RECFSTs is established, which covers a wider range of commonly used material strengths and cross-sectional dimensions. Three machine learning prediction models of this database are then developed, respectively, using different algorithms. The robustness of the machine learning models is evaluated by predicting the axial load-bearing capacity of 60 RECFST specimens from existing references. The results demonstrated that the machine learning models provided superior predictive accuracy compared to theoretical or code-based formulas. A graphical user interface (GUI) is ultimately developed based on the machine learning prediction models to predict the axial load-bearing capacity of RECFST. This tool facilitates rapid and accurate RECFST design.

This paper investigated a new speed regulator using an adaptive transitioning policy iteration learning technique for the variable reluctance motor (VRM) drive. A transitioning strategy is used in this unique scheme to handle the nonlinear behavior of the VRM by using a series of learning centers, each of which is an individual local learning controller at linear operational location that grows throughout the system’s nonlinear domain. This improved control technique based on an adaptive dynamic programming algorithm is developed to derive the prime solution of the infinite horizon linear quadratic tracker (LQT) issue for an unidentified dynamical configuration with a VRM drive. By formulating a policy iteration algorithm for VRM applications, the speed of the motor shows inside the machine model, and therefore the local centers are directly affected by the speed. Hence, when the speed of the rotor changes, the parameters of the local centers grid would be updated and tuned. Additionally, a multivariate transition algorithm has been adopted to provide a seamless transition between the Q-centers. Finally, simulation and experimental results are presented to confirm the suggested control scheme’s efficacy.

This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine (TBM) dataset for performance prediction and boring efficiency optimization using machine learning methods. The big dataset was collected during the Yinsong water diversion project construction in China, covering the tunnel excavation of a 20 km-section with 199 items of monitoring metrics taken with an interval of one second. The research papers were the result of a call for contributions during a TBM machine learning contest in 2019 and covered a variety of topics related to the intelligent construction of TBM. This review comprises two parts. Part I is concerned with the data processing, feature extraction, and machine learning methods applied by the contributors. The review finds that the data-driven and knowledge-driven approaches in extracting important features applied by various authors are diversified, requiring further studies to achieve commonly accepted criteria. The techniques for cleaning and amending the raw data adopted by the contributors were summarized, indicating some highlights such as the importance of sufficiently high frequency of data acquisition (higher than 1 second), classification and standardization for the data preprocessing process, and the appropriate selections of features in a boring cycle. The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers. The ensemble and deep learning methods have found wide applications. Part I highlights the important features of the individual methods applied by the contributors, including the structures of the algorithm, selection of hyperparameters, and model validation approaches.

After 70 years of intricate development, machine learning, represented by deep learning, is based on the multilevel structure of the human brain and the layer-by-layer analysis and processing mechanism of neuron connection and interaction information. The powerful parallel information processing ability of self-adaptation and self-learning has allowed for breakthroughs in many fields, among which the most representative is image recognition. Therefore, this paper proposed optimizing the RBF algorithm with machine learning (ML) to improve the recognition rate of spectrum sensing. The results showed that the average detection success rates of the RBF algorithm were 93.62%, 95.07%, 96.91%, 98.78% and 99.37% when the SNRs were − 8 dB, − 4 dB, 0 dB, 4 dB and 8 dB, respectively, and the other conditions were kept the same. The average detection success rates of the SVM/RBF algorithm were 97.65%, 99.63%, 99.76%, 99.91% and 99.88%, respectively. The average detection success rate of the SVM/RBF algorithm was significantly higher than that of the RBF algorithm. This indicates that analyzing the RBF neural network algorithm through ML can improve the success rate of spectrum sensing, which highlights a new direction for the application of ML and neural networks. [ABSTRACT FROM AUTHOR]

• PCDD/Fs data in 190 flue gas samples from 4 MSWIs were statistically evaluated. • 23478-PeCDF, 123678-HxCDF, 234678-HxCDF were the indicators. • LR, BP ANN and RF were applied to predict PCDD/Fs TEQ levels using indicators. • RF is considered as the more accurate model to predict PCDD/Fs TEQ levels. Monitoring PCDD/Fs emissions from municipal solid waste incinerations (MSWIs) is of paramount importance, yet it can be time-consuming and labor-intensive. Predictive models offer an alternative approach for estimating their levels. However, robust models specific to PCDD/Fs were lacking. In this study, we collected 190 PCDD/Fs samples from 4 large-scale MSWIs in China, with the average PCDD/Fs levels and TEQ levels of 0.987 ng/m3 and 0.030 ng TEQ/m3, respectively. We developed and evaluated predictive models, including traditional statistical methods, e.g., linear regression (LR) as well as machine learning models such as back propagation-artificial neural networks (BP ANN) and random forest (RF). Correlation analysis identified 2,3,4,7,8-PeCDF, 1,2,3,6,7,8-HxCDF, 2,3,4,6,7,8-HxCDF were better indicator congeners for PCDD/Fs estimation (R2 > 0.9, p < 0.001). The predictive results favored the RF model, exhibiting a high R2 value and low root mean square error (RMSE) and mean absolute error (MAE). Additionally, the RF model showed excellent prediction ability during external validation, with low absolute relative error (ARE) of 10.9 %-12.6 % for the three indicator congeners in the normal PCDD/F TEQ levels group (<0.1 ng TEQ/m3) and slightly higher ARE values (13.8 %-17.9 %) for the high PCDD/F TEQ levels group (>0.1 ng TEQ/m3). In conclusion, our findings strongly support the RF model's effectiveness in predicting PCDD/Fs TEQ emission from MSWIs. [ABSTRACT FROM AUTHOR]

Introduction: The high incidence of breast cancer (BC) prompted us to explore more factors that might affect its occurrence, development, treatment, and also recurrence. Dysregulation of cholesterol metabolism has been widely observed in BC; however, the detailed role of how cholesterol metabolism affects chemo- sensitivity, and immune response, as well as the clinical outcome of BC is unknown. Methods: With Mendelian randomization (MR) analysis, the potential causal relationship between genetic variants of cholesterol and BC risk was assessed first. Then we analyzed 73 cholesterol homeostasis-related genes (CHGs) in BC samples and their expression patterns in the TCGA cohort with consensus clustering analysis, aiming to figure out the relationship between cholesterol homeostasis and BC prognosis. Based on the CHG analysis, we established a CAG&lowbar;score used for predicting therapeutic response and overall survival (OS) of BC patients. Furthermore, a machine learning method was adopted to accurately predict the prognosis of BC patients by comparing multi-omics differences of different risk groups. Results: We observed that the alterations in plasma cholesterol appear to be correlative with the venture of BC (MR Egger, OR: 0.54, 95% CI: 0.35-0.84, p<0.006). The expression patterns of CHGs were classified into two distinct groups(C1 and C2). Notably, the C1 group exhibited a favorable prognosis characterized by a suppressed immune response and enhanced cholesterol metabolism in comparison to the C2 group. In addition, high CHG score were accompanied by high performance of tumor angiogenesis genes. Interestingly, the expression of vascular genes (CDH5, CLDN5, TIE1, JAM2, TEK) is lower in patients with high expression of CHGs, which means that these patients have poorer vascular stability. The CAG&lowbar;score exhibits robust predictive capability for the immune microenvironment characteristics and prognosis of patients (AUC=0.79). It can also optimize the administration of various first-line drugs, including AKT inhibitors VIII Imatinib, Crizotinib, Saracatinib, Erlotinib, Dasatinib, Rapamycin, Roscovitine and Shikonin in BC patients. Finally, we employed machine learning techniques to construct a multi-omics prediction model (Risklight),with an area under the feature curve (AUC) of up to 0.89. Conclusion: With the help of CAG&lowbar;score and Risklight, we reveal the signature of cholesterol homeostasis-related genes for angiogenesis, immune responses, and the therapeutic response in breast cancer, which contributes to precision medicine and improved prognosis of BC. [ABSTRACT FROM AUTHOR]

• The potential of optical and SAR data was explored for estimating grassland height. • The RF, GBRT, and CART models were used to estimate grassland height. • GBRT model performed than RF and CART models for grassland height estimation. • The optical remote sensing data performed better than SAR remote sensing data. • Coupling optical and SAR data can effectively improve the estimation accuracy. Grassland height is an important indicator used to evaluate ecological environments in grasslands. The study explored the potential of optical and SAR (synthetic aperture radar) remote sensing data in estimating grassland height with machine learning methods and constructed the grassland height remote sensing inversion model. Then the mean grassland height in August 2015 was estimated with the inversion model in Inner Mongolia Autonomous Region, China. Among classification and regression tree (CART), random forest (RF), and gradient boosting regression tree (GBRT) models, the GBRT model had the highest estimation accuracy. There was little correlation between SAR data and grassland height, and SAR data produced poor accuracy for grassland height estimation. The grassland height could be better estimated by optical remote sensing data. The best estimation accuracy (GBRT model: for training data: R2 = 0.71, RMSE = 3.58 cm, P < 0.01; for test data: R2 = 0.58, RMSE = 3.94 cm, P < 0.01) was achieved by the combination of optical and SAR remote sensing data. However, SAR data played an auxiliary role, and the estimation of grassland height was mainly realized by optical data. The average vegetation height of the grasslands in Inner Mongolia in August 2015 was 19.15 cm, gradually decreasing from northeast to southwest. The study proposed a high-precision method for estimating grassland height, which provided a basis for studying grassland environments and conditions. [ABSTRACT FROM AUTHOR]