Mean Square Error Research Articles

An ensemble learning model for forecasting water-pipe leakage

Based on the benefits of different ensemble methods, such as bagging and boosting, which have been studied and adopted extensively in research and practice, where bagging and boosting focus more on reducing variance and bias, this paper presented an optimization ensemble learning-based model for a large pipe failure dataset of water pipe leakage forecasting, something that was not previously considered by others. It is known that tuning the hyperparameters of each base learned inside the ensemble weight optimization process can produce better-performing ensembles, so it effectively improves the accuracy of water pipe leakage forecasting based on the pipeline failure rate. To evaluate the proposed model, the results are compared with the results of the bagging ensemble and boosting ensemble models using the root-mean-square error (RMSE), the mean square error (MSE), the mean absolute error (MAE), and the coefficient of determination (R2) of the bagging ensemble technique, the boosting ensemble technique and optimizable ensemble technique are higher than other models. The experimental result shows that the optimizable ensemble model has better prediction accuracy. The optimizable ensemble model has achieved the best prediction of water pipe failure rate at the 14th iteration, with the least RMSE = 0.00231 and MAE = 0.00071513 when building the model that predicts water pipe leakage forecasting via pipeline failure rate.

BSEO-Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices.

Microfluidic models have become essential instruments for studying enhanced oil recovery techniques through fluid and chemical injection into micromodels to observe interactions with pore structures and resident fluids. The widespread use of cost-effective lab-on-a-chip devices, known for efficient data extraction and minimal reagent usage, has driven demand for efficient data management methods crucial for high-performance data and image analyses. This article introduces a semiautomatic method for calculating oil recovery in polymeric nanofluid flooding experiments based on the background subtraction (BSEO). It employs the background subtraction technique, generating a foreground binary mask to detect injected fluids represented as pixel areas. The pixel difference is then compared to a threshold value to determine whether the given pixel is foreground or background. Moreover, the proposed method compares its performance with two other representative methods: the ground truth (manual segmentation) and Fiji-ImageJ software. The experiments yielded promising results. Low values of mean-squared error (MSE), mean absolute error (MAE), and root-mean-squared error (RMSE) indicate minimal prediction errors, while a substantial coefficient of determination (R2) of 98% highlights the strong correlation between the method's predictions and the observed outcomes. In conclusion, the presented method emphasizes the viability of BSEO as a robust alternative, offering the advantages of reduced computational resource usage and faster processing times.

SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

A complexity efficient penta-diagonal quantum smoothing filter for bio-medical signal denoising: a study on ECG

Extracting information-bearing signal from a noisy environment has been a practical challenge in both classical and quantum computing formalism, especially in critical signal processing applications. To filter out the effect of noise, we propose a quantum smoothing filter built upon quantum formalism-based circuits applied for electrocardiogram signal denoising. The proposed quantum filter is a conceptually novel framework with an advantage in computational complexity as compared to the existing classical filters, such as discrete wavelet transform and empirical mode decomposition, whereas it achieves similar performance metrics for the accuracy of the filter. Further, we exploit the penta-diagonal Toeplitz structure of the smoothing filter, which gives approximately 48%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$48\\%$$\\end{document} gate cost reduction for 10 qubit circuit compared to the standard Hamiltonian simulation without structure. The run-time complexity using the quantum matrix inversion technique for the structured matrix is given by O~κ2poly(logN)εP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ilde{{\\mathscr {O}}}\\left( \\frac{\\kappa ^2 \ ext {poly}(\\log {N})}{\\varepsilon _P}\\right)$$\\end{document} for condition number κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document} of the N×N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\ imes N$$\\end{document} filter matrix within precision εP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon _P$$\\end{document}. Embedding fixed sparsity of the banded matrix, the quantum filter shows potentially better run-time complexity than classical filtering techniques. For the quantifiable research results of our work, we have shown several performance metrics, such as mean-square error and peak signal-to-noise ratio analysis, with a bound of error due to observation noise, simulation error and quantum measurement uncertainty.

Revealing the urban heat Island: Investigating spatiotemporal surface temperature dynamics, modeling, and interactions with controllable and non-controllable factors

The unchecked growth of urban populations has spurred rapid and unsustainable urban expansion, exacerbating environmental degradation both locally and globally. This research offers a comprehensive analysis of the Urban Heat Island (UHI) phenomenon in Bangalore and Hyderabad, India, focusing on its spatial and temporal distribution and its correlation with air pollutants. Conducted over the course of summer and winter seasons from 2001 to 2021, this study advances our understanding of UHI dynamics in rapidly urbanizing regions, shedding light on the complex interplay between urbanization, climate, and air quality. The primary objective of this study is to assess and compare the predictive performance of baseline Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models for forecasting Land Surface Temperature (LST) in Hyderabad, India, while examining the impact of integrating spatial information into these models. Incorporating an extensive array of input variables, ranging from spectral indices to terrain features, land use/land cover (LULC) data, atmospheric parameters, and spatial data, the analysis reveals significant relationships between LST and various factors. Notably, Aerosol Optical Depth (AOD) and Normalized Difference Built-up Index (NDBI) exhibit strong positive correlations with LST, while Enhanced Vegetation Index (EVI) demonstrates a pronounced negative correlation. Similarly, Modified Normalized Difference Water Index (MNDWI) displays the strongest positive correlation with LST (r = 0.49), whereas EVI exhibits the most notable negative correlation (r = −0.67). Additionally, Modified Bare Soil Index (MBI) demonstrates a noteworthy negative correlation (r = −0.48) with LST for Bangalore. Atmospheric components such as CO, NO2, and O3 also show significant positive correlations with LST, with NO2 displaying the highest correlation during winter in urban areas. XGBoost emerges as the superior model, achieving high R2 values of 0.84 for Bangalore and 0.88 for Hyderabad. Integration of spatial information consistently enhances the performance of both RF and XGBoost models, resulting in reduced Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values. However, predictions over water bodies tend to be underestimated. The study underscores the importance of incorporating spatial data for accurate LST prediction, with implications for environmental monitoring and management. Despite the advancements made, further research is needed to elucidate the specific mechanisms through which spatial information improves model performance, suggesting avenues for future investigation and refinement of modeling methodologies. At the intersection of rapid urban growth and environmental conservation, these findings are poised to inform policymakers, urban planners, and researchers, guiding them towards innovative and sustainable urban development strategies aimed at mitigating the adverse impacts of rapid urban expansion while promoting ecosystem well-being. The study contributes to spatial modeling strategies and refines LST prediction methodologies, offering valuable insights for addressing the challenges posed by urbanization and climate change in rapidly urbanizing regions.

Optical tunability with superlattice hyperbolic metamaterial coatings

Optical tunability is demonstrated with multi-metal hyperbolic metamaterial (HMM) structures on varying substrate surfaces. A systematic uniaxial modeling methodology is adapted from classical approaches to extract the anisotropic permittivity of the films to identify epsilon-near-zero (ENZ) behavior over a wide range of metal volume fractions (φ). The anisotropic model accuracy is quantified by a mean squared error (MSE) with the presented modeling routine resulting in a MSE < 35 for all samples. The optical model thicknesses were verified with transmission electron microscopy (TEM) imaging and measurements. This study also demonstrates the ability to tune the optical responses in the visible-to-infrared frequencies with surface microtexturing and applied strain with HMM coatings on flexible Kapton substrates. Furthermore, the structured films and modeling procedure lay the groundwork for future studies on coupling the anisotropic optical and thermal properties for control over advanced multifunctional thin-film coatings.

Machine learning prediction of health risk and spatial dependence of geogenic contaminated groundwater from the Hetao Basin, China

Geogenic contaminated groundwater (GCG), characterized by elevated arsenic, fluoride, and iodine levels, present a significant challenge to public health and government management. Conventional survey-based approaches of collecting groundwater samples, conducting physicochemical tests, and performing spatial interpolation to obtain regional groundwater chemical component maps are inefficient and costly. More importantly, it does not take into account the actual hydrogeological conditions or the characteristics of pollutant transport and enrichment. To address this issue, we utilized Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to analyze the likelihood of occurrence of arsenic, fluoride, and iodine as well as their spatial distribution in shallow groundwater from the Hetao Basin. Our study incorporated 20 indicators related to meteorology, soil physicochemical properties, and groundwater conditions, along with 1505 labeled samples consisting of groundwater arsenic, fluoride, and iodine concentrations and their corresponding coordinates. Subsequently, the study automatically analyzed the meteorological, soil physicochemical properties and groundwater conditions by constructing a machine learning model using the available data. In order to optimise and select the best prediction model, this paper presents a quantitative evaluation of the prediction performance of various machine learning models. The accuracy (AC), area under curve (AUC) and mean squared error (MSE) were calculated to predict the spatial distribution of CGC. Subsequently, the optimized model for predicting the spatial distribution of GCG was selected. The results showed that the XGBoost algorithm provided optimal predictions for groundwater with arsenic concentrations above 10 μg/L and fluoride concentrations exceeding 1.5 mg/L, whereas the RF model provided the best predictions for groundwater with arsenic concentrations surpassing 50 μg/L and iodine concentrations exceeding 100 μg/L. Subsequently, groundwater health risk zones were delineated based on an optimal prediction model, and demographic analysis was conducted in both the direct and potential groundwater risk zones. Model predictions indicated that hundreds of thousands of people in the Hetao Basin were facing a public health crisis caused by high concentrations of arsenic, fluoride and iodine in groundwater. These findings underscore the significant health challenge in the study area. Considering the agricultural development and increasing groundwater use in the area, our findings can guide local governments in managing the extent of groundwater development, establishing control zones, and enhancing protection measures for populations at risk from groundwater contamination.

Numerical treatment for radiative hybrid nanofluid flow over a stretching sheet

Artificial neural networks (ANNs) have brought a huge transformation to the machine learning regime by furnishing unparalleled abilities for modeling intricate phenomena and coping with a variety of challenges. In the oneiric field of ANNs, the essential approach for training ANNs is backpropagation. However, optimization is crucial when handling the complex fluid flow phenomenon through backpropagation. The current article implements the Levenberg Marquardt Technique with ANN backpropagation (LMT-BP-ANN) to investigate the radiative flow of a hybrid nanofluid (RHNF) addressing a Cattaneo-Christov flux system past a stretching sheet (SS). Fe2O3 with single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) hybrid nanofluid is considered. The base fluid is ethylene glycol. Physical parameters of nanofluidic system, including the magnetic parameter M, the solid volume fractions φ1 and φ2, the velocity slip parameter λ, the relaxation time parameter Ω, the Biot number ϒ, and the thermal radiation parameter Gr are evaluated through appropriate variations that effectively accomplish the dynamics of the fluid model. These are utilized to create the dataset for LMT-BP-ANNs using the renowned deterministic Adam's numerical technique in Mathematica software. In MATLAB software, 70 % of data is kept for preparing, 15 % for checking, and 15 % for validating the neural network. The performance plot, regression graphs, and error histograms are constructed to demonstrate the suggested LMT-BP neural network scheme's high efficiency, efficacy, and exactness. When ANN performs, a mean squared error (MSE) of order 10−11 is observed. The observed R squared value is 1. As the magnetic parameter estimate grows, the fluid velocity declines, although the thermal profile shows increasing behavior. As the velocity slip parameter increases, the velocity of the fluid drops. Key findings of this study are anticipated to have a noteworthy bearing on sectors that require local refrigeration and heating by contravention.

Post-Fracture Production Prediction with Production Segmentation and Well Logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV

As the petroleum industry increasingly exploits unconventional reservoirs with low permeability and porosity, accurate predictions of post-fracture production are becoming critical for investment decisions, energy policy development, and environmental impact assessments. However, despite extensive research, accurately forecasting post-fracture production using well-log data continues to be a complex challenge. This study introduces a new method of data volume expansion, which is to subdivide the gas production of each well on the first day according to the depth of logging data, and to rely on the correlation model between petrophysical parameters and gas production to accurately combine the gas production data while matching the accuracy of the well-log data. Twelve pipelines were constructed utilizing a range of techniques to fit the regression relationship between logging parameters and post-fracture gas production These included data preprocessing methods (StandardScaler and RobustScaler), feature extraction approaches (PCA and PolynomialFeatures), and advanced machine learning models (XGBoost, Random Forest, and neural networks). Hyperparameter optimization was executed via GridSearchCV. To assess the efficacy of diverse models, metrics including the coefficient of determination (R2), standard deviation (SD), Pearson correlation coefficient (PCC), mean absolute error (MAE), mean squared error (MSE), and root-mean-square error (RMSE) were invoked. Among the several pipelines explored, the PFS-NN exhibited excellent predictive capability in specific reservoir contexts. In essence, integrating machine learning with logging parameters can be used to effectively assess reservoir productivity at multi-meter formation scales. This strategy not only mitigates uncertainties endemic to reservoir exploration but also equips petroleum engineers with the ability to monitor reservoir dynamics, thereby facilitating reservoir development. Additionally, this approach provides reservoir engineers with an efficient means of reservoir performance oversight.

Neural network design for non-Newtonian Fe3O4–blood nanofluid flow modulated by electroosmosis and peristalsis

In this study, we present a novel approach that utilizes the Levenberg–Marquardt algorithm (LMA) based on artificial neural networks (ANNs) to evaluate the flow characteristics of a thermally evolved blood-based nanofluid in the presence of peristalsis and electroosmosis. The Casson fluid model is employed to govern the non-Newtonian characteristics observed in the flow of blood. In addition, the thermal properties of the nanofluidic medium in contact with platelet magnetite nanomaterials are also studied in detail. Further, the effects of thermal radiation, thermal buoyancy force, magnetic field and Joule heating are also given due consideration. The mathematically formulated two-dimensional equations describing the flow of Casson liquid are brought into their dimensionless form under the lubrication theory. A dataset for the proposed ANN models is generated to explore various scenarios of the fluidic model by varying the pertinent parameters using NDSolve in Mathematica. The computational approach utilizing LMA is deployed across three distinct phases of performance assessment, distributing the data into training, testing and validation sets at the proportions of 80%, 10% and 10%, respectively. This implementation involves the utilization of 10 hidden neurons. The utilization of regression analysis for testing, mean-squared error calculation, error histograms and correlation assessment in numerical replications of the ANNs is also examined to verify their capability, accuracy, validity and effectiveness. This study is crucial for understanding the peristaltic blood transportation in small blood vessels of living organisms.

Predictive Modeling of Heart Rate Dynamics based on Physical Characteristics and Exercise Parameters: A Machine Learning Approach

To accurately forecast heart rate changes during exercise, which is essential for customized health monitoring and improving training regimens, it is necessary to comprehend both the physiological foundations and the technical capacities for data processing. This research utilizes Machine Learning (ML) methodologies to predict heart rate reactions based on physical characteristics and activity variables. Our research focuses on the health and sports aspects of our results, using a comprehensive dataset that includes a wide range of activity types and ambient circumstances across 12,000 sets. We establish a connection between the ability of models such as Linear Regression (LR) and Extreme Gradient Boosting (XGB) to predict outcomes and their practical use in exercise management and optimizing athlete performance. These models accurately forecast variations in heart rate and also provide insights into the cardiovascular demands of various physical activities. Standard metrics measure the effectiveness of these models. The Linear Regression (LR) model achieved a Mean Absolute Error (MAE) of 0.419, a Mean Squared Error (MSE) of 0.294, a Root Mean Squared Error (RMSE) of 0.543, and an R-Squared value of 0.997. On the other hand, the Extreme Gradient Boosting (XGB) Regressor model achieved a Mean Absolute Error (MAE) of 0.421, a Mean Squared Error (MSE) of 0.335, a Root Mean Squared Error (RMSE) of 0.578, and an R-Squared value of 0.996. These metrics demonstrate the usefulness of these models in real-world scenarios. Our study's findings demonstrate that the combination of physiological data and powerful machine learning models may improve an individual's comprehension of fitness levels and the requirements for adaptive training. This study not only adds to the field of computational physiology, but it also aids in the creation of adaptive, real-time therapies for improving health and performance.

Artificial Intelligence-Driven Model for Gold Price Prediction

Artificial Intelligence-Driven Model for Gold Price Prediction

Application of machine learning on hydrate formation prediction of pure components with water and inhibitors solution

AbstractThe present work investigates the use of machine learning approaches for the prediction of hydrate formation pressure (HFP) in gas hydrate systems. Advanced machine learning models, including the decision tree regressor (DTR), random forest regressor (RFR), extreme gradient boosting (XGB), gradient boosting regressor (GBR), histogram gradient boosting regressor (HGBR), and CatBoost regressor (CB), are trained and evaluated on a large dataset consists of 3137 experimental data points. The models are evaluated using R‐squared (R2), mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). The study indicates that for the intent of HFP prediction, CatBoost outperformed all other machine learning models. It demonstrated high accuracy on the testing set with an R2 value of 0.9922, and with the lowest RMSE (1.61 × 10−3), MAE (7.90 × 10−4), and MSE (2.58 × 10−6), CatBoost strengthened its prediction ability.

Artificial Neural Networks and Experimental Analysis of the Resistance Spot Welding Parameters Effect on the Welded Joint Quality of AISI 304.

The automobile industry relies primarily on spot welding operations, particularly resistance spot welding (RSW). The performance and durability of the resistance spot-welded joints are significantly impacted by the welding quality outputs, such as the shear force, nugget diameter, failure mode, and the hardness of the welded joints. In light of this, the present study sought to determine how the aforementioned welding quality outputs of 0.5 and 1 mm thick austenitic stainless steel AISI 304 were affected by RSW parameters, such as welding current, welding time, pressure, holding time, squeezing time, and pulse welding. In order to guarantee precise evaluation and experimental analysis, it is essential that they are supported by a numerical model using an intelligent model. The primary objective of this research is to develop and enhance an intelligent model employing artificial neural network (ANN) models. This model aims to provide deeper knowledge of how the RSW parameters affect the quality of optimum joint behavior. The proposed neural network (NN) models were executed using different ANN structures with various training and transfer functions based on the feedforward backpropagation approach to find the optimal model. The performance of the ANN models was evaluated in accordance with validation metrics, like the mean squared error (MSE) and correlation coefficient (R2). Assessing the experimental findings revealed the maximum shear force and nugget diameter emerged to be 8.6 kN and 5.4 mm for the case of 1-1 mm, 3.298 kN and 4.1 mm for the case of 0.5-0.5 mm, and 4.031 kN and 4.9 mm for the case of 0.5-1 mm. Based on the results of the Pareto charts generated by the Minitab program, the most important parameter for the 1-1 mm case was the welding current; for the 0.5-0.5 mm case, it was pulse welding; and for the 0.5-1 mm case, it was holding time. When looking at the hardness results, it is clear that the nugget zone is much higher than the heat-affected zone (HZ) and base metal (BM) in all three cases. The ANN models showed that the one-output shear force model gave the best prediction, relating to the highest R and the lowest MSE compared to the one-output nugget diameter model and two-output structure. However, the Levenberg-Marquardt backpropagation (Trainlm) training function with the log sigmoid transfer function recorded the best prediction results of both ANN structures.

Multi-Image Steganography

Steganography is the practice of concealing information within various types of data, encompassing the embedding of confidential content like messages, images, audio, or video within a cover image. The primary objective involves hiding the secret message or image within the selected image through the utilization of the Least Significant Bit (LSB) technique. To ensure the safeguarding and security of the concealed content, the Advanced Encryption Standard (AES) Algorithm is implemented. The comparison of different image formats, each varying in text length or image size, is conducted. The algorithm's efficacy is evaluated through metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE), with a higher PSNR value indicating a superior image quality. Steganography assumes a vital role in numerous applications, including but not limited to medical, military, One Time Password (OTP), and copyright protection. Key Words: Image steganography, LSB, Advanced Encryption Standard, PSNR Ratio, MSE

Energy-Efficient Resource Optimization for IRS-Assisted VLC-Enabled Offshore Communication System

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

Compressive strength prediction of Portland cement clinker using machine learning by XRD data

Accurately predicting the 28-day compressive strength of Portland cement clinkers is challenging due to their complex multiphase nature, especially when production processes and environmental conditions vary. To address this issue, the TransXRD model was introduced, which is a novel deep learning approach that combines the Transformer architecture with CNN design. 214 clinker samples were collected from 6 cement plants. The diffraction peak sequence of samples ranging from 10° to 55° was used as required input for the model. A combination of one-dimensional convolution is incorporated to extract local features, and self-attention mechanisms are leveraged to perform global feature integration. XRD data were directly utilized, bypassing additional processing to minimize signal loss. Compared to other DNN (deep neural network) models, the TransXRD model demonstrates promising performance, achieving a root mean square error (RMSE) of 1.15, a mean squared error (MSE) of 1.33, a mean absolute error (MAE) of 0.93, and a mean absolute percentage error (MAPE) of 1.76 on the test set. Notably, in the training set, 100% of the data exhibit prediction errors within 2 MPa, while within the test set, 90% of the prediction errors are within 2 MPa. The application potential of XRD data in predicting the compressive strength of cement clinker also offers novel insights into predicting the performance of other materials.

Predicting Bus Travel Time in Cheonan City Through Deep Learning Utilizing Digital Tachograph Data

Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

Optimization of process parameters of selective laser melted nickel-based superalloy for densification by random forest regression algorithm and response surface methodology

In this study, Random Forest Regression (RFR) and Response Surface Methodology (RSM) were employed to predict the optimized processing parameters to achieve the highest densification of a nickel-based superalloy Mar-M247LC fabricated by selective laser melting (SLM). The RFR model considered input processing parameters such as laser power, hatch distance, and scanning speed. A dataset of 223 samples, was used to train the RFR model. As a result, the RFR model exhibited accuracy of 99.57 %, R2 value of 0.976, Mean Square Error (MSE) of 0.402, and Mean Absolute Percentage Error (MAPE) of 0.426 % on testing set. In addition to the RFR model, this study also employed Central Composite Design (CCD) and RSM to optimize the processing parameter sets. Subsequently, this study conducted Box-Behnken Design (BBD) to experimentally validate the accuracy of this RFR model. In the end, a set of the optimal processing parameters was tested and resulted a sample densification of 99.959 %, outperformed that in the original database before building the RFR model, which was 99.734 %. In summary, the RFR models was able to predict densification with accuracy, and by coupling with RSM, the optimal processing parameter could be obtained, so better densification of the build could be achieved.

Unveiling a Novel Solvatomorphism of Anti-inflammatory Flufenamic Acid: X-ray Structure, Quantum Chemical, and In Silico Studies.

This paper delves into the polymorphism of 2-[3-(trifluoromethyl)anilino]benzoic acid, commonly referred to as flufenamic acid (FA), a pharmaceutical agent employed in treating inflammatory conditions. The central focus of the study is on a newly unearthed solvatomorphic structure of FA in methanol (FAM), and a thorough comparison is conducted with the commercially available standard structure. Employing a comprehensive approach, including X-ray crystallography, Hirshfeld surface analysis, density functional theory (DFT), molecular docking, and molecular dynamics (MD) simulations, the research aims to unravel the structural and functional implications of solvatomorphism. The X-ray crystal structure analysis brings to light notable differences between the standard FA and solvatomorphic FAM, showcasing variations in intermolecular interactions and crystal packing. Key features such as hydrogen bonding, π···π stacking, and C-H···π interactions are identified as influential factors shaping the stability and conformation of the compounds. Hirshfeld surface analysis further quantifies the nature and contribution of intermolecular interactions, providing a comprehensive perspective on molecular stability. Density functional theory offers valuable electronic structure insights, highlighting disparities in frontier molecular orbitals between FA and FAM. Molecular docking studies against prostaglandin D2 11-ketoreductase explore potential drug interactions, unveiling distinct binding modes and hydrogen bonding patterns that shed light on how the solvatomorphic structure may impact drug-target interactions. In-depth molecular dynamics simulations over 100 ns investigate the stability of the protein-ligand complex, with root mean square deviation and root mean square fluctuation analyses revealing minimal deviations and affirming the stability of FAM within the active site of the target protein.