Absolute Percentage Error Research Articles

Sustainability-driven application of waste steel and tyre rubber fibres as reinforcement in concrete: An optimization study using response surface methodology

Innovative use of waste materials has proven to be effective for improving concrete’s technical properties while eliminating the threat of environmental pollution. There is paucity of research regarding the combined use of Waste Steel Fibres (WSFs) and Waste Tire Rubber Fibres (WTRFs) in concrete. Thus, this study aimed to investigate the influence of WSFs and WTRFs as reinforcement in concrete and determine their optimal fractions for enhancing the concrete strength properties using Response Surface Methodology (RSM). WSFs of 0.3-1.5% with a 0.6% increment and WTRFs of 0.3-1.5% with a 0.6% increment by concrete volume as reinforcement in concrete with water-to-cement ratio (W/C) of 0.25–0.65 with 0.2 increment were designed using the Box-Behnken Design of RSM. A total of fourteen (14) concrete mixes with a design strength of 25 N/mm2 were prepared. The fresh and hardened properties (slump, density, water absorption, 7- and 28-day compressive and split tensile strengths) of concrete were determined using standard procedures. The RSM was used to evaluate the interaction between the concrete variables and identify their optimum combination which gave the peak values of responses. Furthermore, the characteristics and sustainability of the concrete under optimum variables were compared with that of the conventional concrete. The outcomes revealed that the inclusion of WSFs and WTRFs yielded concretes with maximum slump, density, water absorption and 28-day compressive and split tensile strengths of 25 mm, 2633 kg/m3, 5%, 41 N/mm2 and 4.54 N/mm2, respectively. The optimization technique showed the optimal variables combination which yielded the peak response values of WSFs (1.40%), WTRFs (0.66%) and W/C (0.54). The nominal variance with the absolute percent error of less than (9%) between the actual experimental verified results and predicted results for all the responses validates the predictability of the model. The split tensile strength and compressive strength increased by 28.33% and 48.85%, respectively at the optimum setpoint of variable with respect to the reference concrete. In addition, the WSFs and WTRFs-reinforced concrete exhibited lower embodied CO2 emission but the embodied energy and cost are slightly higher relative to conventional concrete. Nevertheless, the embodied energy of the concrete was significantly lesser than that of concrete that used individual fibers for reinforcement. Thus, the enhancement of concrete strength properties with the prospect for sustainable fibre-reinforced concrete production through the use of waste steel and tyre rubber fibres is feasible.

Short-term air pollution prediction using graph convolutional neural networks

Pollution is a major concern in the present day, causing multiple illnesses and deaths, specifically in developing countries in Asia and Africa. While it has drawn worldwide attention as governments try to issue laws to meet certain criteria for air pollution levels, pollution concentration forecasting has become a major challenge. Particularly, short term forecasting will help to gain information regarding concentrations of harmful pollutants for the upcoming hours and enable better decision-making with regards to controlling air pollution. In this paper, we investigate spatio-temporal graph-based models to determine the best methods for spatial and temporal analysis of data. The models have the additional capacity to perform multi-variate predictions of correlated data, i.e., predicting multiple pollutant concentrations simultaneously, thus requiring lower computational efforts. A real-world pollution dataset measured over Delhi, India, is used to comparing the proposed models with baselines, which shows the Spatio-Temporal Graph Convolution Neural Network (STGCN) models to be performing better than others. For a better understanding of model architectures with the most effective strategies for spatial and temporal data analysis, three models, namely STGCN-A, STGCN-B, STGCN-C have been developed. The models have been compared with 6 other baselines over multiple forecasting horizons of 1 h, 24 h, and 48 h timesteps using various metrics such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE). On the PM2.5 dataset of Delhi, STGCN-B achieves a performance of 10.53 MAE, 6.92 RMSE and 25.25 MAPE for a 1 h forecast, while STGCN-C achieves 20.18 MAE, 14.73 RMSE and 55.45 MAPE for a 24 h forecast. In general, both structures achieve similar results, with STGCN-C being better in many cases. They are further analysed through observation-prediction graphs and Taylor diagrams, which give an insight into our findings. The models are additionally validated on a benchmark real-world dataset from California, USA for better understanding of the spatio-temporal relations and model performances on a more stable dataset, where STGCN-C performs best for PM2.5 with 4.30 RMSE, 1.98 MAE, 25.96 MAPE for 1 h predictions for univariate data and 3.63 RMSE, 1.88 MAE and 25.91 MAPE in multivariate forecasting. The developed spatio-temporal graph-based models hold promising applications in urban air quality management, aiding policymakers in implementing targeted interventions to mitigate pollution-related health risks. Furthermore, these models can support public health agencies by providing timely and accurate forecasts of pollutant concentrations, enabling proactive measures to safeguard community well-being. Our study showcases the efficacy of spatio-temporal graph-based models in accurately forecasting air pollutant concentrations, with particular emphasis on short-term predictions. By leveraging multi-variate capabilities, our proposed models demonstrate superior performance compared to baseline approaches across various forecasting horizons.

Recognizing the Most Accurate Herschel-Bulkley Fluid Pressure Estimation Method for Annulus: An Update to API RP 13D, 7th Edition

Summary The Herschel-Bulkley (HB) model is widely regarded as highly accurate for modeling non-Newtonian fluid flow, applicable across diverse fields such as chemical, mechanical, and petroleum engineering. The American Petroleum Institute and other references recognize the HB model as the most accurate and recommend the industry use it for drilling hydraulics. Pressure estimation models are beneficial for applying the HB model in field settings, as numerical solutions may not be feasible due to the computational demands and time constraints, especially challenging for high-frequency real-time pressure loss estimation (at least 1 Hz). Past literature has presented four estimation methods for the HB fluid, consisting of Zamora and Lord (1974), Merlo et al. (1995), Gjerstad and Time (2015), and the generalized method. However, the recommended practice API RP 13D (2023), “Rheology and Hydraulics of Oilwell Drilling Fluids,” has considered the most traditional Zamora and Lord (1974) as their chosen estimation method for industry use while neglecting to identify the most accurate model. Therefore, to update API RP 13D (2023), this work addresses this shortcoming by investigating the performance of different methods in estimating pressure losses while providing user-friendly, straightforward procedures applicable for field use. To recognize the most accurate estimation method for industry use, different methods’ pressure losses were estimated and compared with the numerical solution as well as experimental data to find the errors. The results showed that the Merlo et al. (1995) method provides the most accurate estimates, with the least average absolute percent error (APE) of maximum 4% underestimating the numerical solution, followed by Zamora and Lord’s error overestimating the numerical solution. The errors may slightly vary depending on the input parameters used, including diameters, mud properties, and circulation rate. The effect of the circulation rate on the accuracy of different methods was also investigated; except for the generalized method, the accuracy of the other methods increases with increasing the circulation rate. This work is a very innovative practical work for finding annular pressure losses accurately without using complex numerical solutions.

Optimal Spectral Performance on Pediatric Photon-Counting CT: Investigating Phantom-Based Size-Dependent kV Selection for Spectral Body Imaging.

The comprehensive evaluation of kV selection on photon-counting computed tomography (PCCT) has yet to be performed. The aim of the study is to evaluate and determine the optimal kV options for variable pediatric body sizes on the PCCT unit. In this study, 4 phantoms of variable sizes were utilized to represent abdomens of newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients. One solid water and 4 solid iodine inserts with known concentrations (2, 5, 10, and 15 mg I/mL) were inserted into phantoms. Each phantom setting was scanned on a PCCT system (Siemens Alpha) with 4 kV options (70 and 90 kV under Quantum Mode, 120 and 140 kV under QuantumPlus Mode) and clinical dual-source (3.0 pitch) protocol. For each phantom setting, radiation dose (CTDIvol) was determined by clinical dose settings and matched for all kV acquisitions. Sixty percent clinical dose images were also acquired. Reconstruction was matched across all acquisitions using Qr40 kernel and QIR level 3. Virtual monoenergetic images (VMIs) between 40 and 80 keV with 10 keV interval were generated on the scanner. Low-energy and high-energy images were reconstructed from each scan and subsequently used to generate an iodine map (IM) using an image-based 2-material decomposition method. Image noise of VMIs from each kV acquisition was calculated and compared between kV options. Absolute percent error (APE) of iodine CT number accuracy in VMIs was calculated and compared. Root mean square error (RMSE) and bias of iodine quantification from IMs were compared across kV options. At the newborn size and 50 keV VMI, noise is lower at low kV acquisitions (70 kV: 10.5 HU, 90 kV: 10.4 HU), compared with high kV acquisitions (120 kV: 13.8 HU, 140 kV: 13.9 HU). At the newborn size and 70 keV VMI, the image noise from different kV options is comparable (9.4 HU for 70 kV, 8.9 HU for 90 kV, 9.7 HU for 120 kV, 10.2 HU for 140 kV). For APE of VMI, high kV (120 or 140 kV) performed overall better than low kV (70 or 90 kV). At the 5-year-old size, APE of 90 kV (median: 3.6%) is significantly higher (P < 0.001, Kruskal-Wallis rank sum test with Bonferroni correction) than 140 kV (median: 1.6%). At adult size, APE of 70 kV (median: 18.0%) is significantly higher (P < 0.0001, Kruskal-Wallis rank sum test with Bonferroni correction) than 120 kV (median: 1.4%) or 140 kV (median: 0.8%). The high kV also demonstrated lower RMSE and bias than the low kV across all controlled conditions. At 10-year-old size, RMSE and bias of 120 kV are 1.4 and 0.2 mg I/mL, whereas those from 70 kV are 1.9 and 0.8 mg I/mL. The high kV options (120 or 140 kV) on the PCCT unit demonstrated overall better performance than the low kV options (70 or 90 kV), in terms of image quality of VMIs and IMs. Our results recommend the use of high kV for general body imaging on the PCCT.

Inverse radius weighting and its python package “IRWPy”: A new topography-informed interpolation to enhance geological interpretations

The extent and volume of geochemical sampling are inherently constrained by numerous factors, including budgetary limitations, analytical costs, and access restrictions. These constraints result in sampling networks that vary in density and regularity, dividing regions into sampled and unsampled areas. The creation of continuous geochemical maps is essential for geochemical prospectivity, which aims to identify geochemical anomalies, distinguish between background levels and anomalies, and define mineralization patterns. Therefore, it is necessary to interpolate data from sampled areas to estimate values in unsampled regions. Although several interpolation models exist, including Inverse Distance Weighting and various kriging methods, Inverse Distance Weighting is often used in two-dimensional ground modeling because, unlike kriging methods, Inverse Distance Weighting has no smoothing effect on edges. Inverse Distance Weighting’s reliance solely on horizontal Euclidean distances between samples overlooks critical factors such as topography and the ensuing effects on dilution, transportation, and element mobility, rendering it less effective over varied elevations. This study introduces Inverse Radius Weighting, a new interpolation technique that incorporates both Euclidean and elevation fluctuations, therefore Pythagorean distance, to help with better geological interpretations. We assessed the efficacy of Inverse Radius Weighting compared to Inverse Distance Weighting across three elements with varying mobility (Arsenic − highly mobile, Copper − moderately mobile, and Barium − nearly immobile), using different numbers of neighbors and by comparing three different evaluation measures, namely R2, Mean Absolute Error and Mean Absolute Percentage Error. By evaluating the spatial uncertainty of the generated maps and selecting the configurations with the least uncertainty as the final maps, our analysis reveals an improvement in correlation between interpolated and actual values with Inverse Haversine Radius Weighting. With this advancement, Inverse Haversine Radius Weighting overcomes the limitations of traditional Inverse Distance Weighting by accounting for elevation and its associated effects, thereby paving the way for more accurate and geochemically meaningful interpolation in geochemical prospectivity mapping.

Digital modelling method of coal-mine roadway based on millimeter-wave radar array

The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway.

Variability and error in measurement of infant formula powder and water: an experimental study.

Formula feeding is the only viable nutrition alternative for infants 0-6mos who cannot breastfeed. Among the drawbacks of formula feeding, however, is potential dilution or concentration errors in the formula during preparation that may lead to infant health issues. The present study aimed to investigate the accuracy of caregiver measurements as they prepared infant formula under multiple conditions, compared with manufacturer specifications. A diverse sample of caregivers (N = 84) participated in this cross-over experimental study. Participants hand-scooped infant formula powder and poured water to prepare 4oz. and 7oz. feedings, using both a standardized set of infant formula products and participants' own products. Linear mixed effects models were used to estimate fixed effects of target amount (4oz. versus 7oz) and products (participant versus researcher) on mean absolute percent error (MAPE) of measurement. Across all conditions MAPE was significantly greater for measuring powder than for water (9.0% vs. 4.4%; p < 0.001) with a combined powder and water MAPE at 13.0%. Greater measurement error was associated with the odd-sized 7oz. preparation and participants' own products. We observed considerable variability and substantial error during infant formula preparation, particularly for hand-scooping of powder, which tended toward higher values than the theoretical gold standard.

Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils

Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial. Three ensemble machine learning models were trained to obtain spatial predictions of the C/N ratio, MAOM, and POM in German agricultural topsoil (0–10 cm). Parameter optimization and model evaluation were performed using nested cross-validation. Additionally, a modification to the regressor chain was applied to capture and interpret the interactions among the C/N ratio, MAOM, and POM. The ensemble models yielded mean absolute percent errors (MAPEs) of 8.2% for the C/N ratio, 14.8% for MAOM, and 28.6% for POM. Soil type, pedo-climatic region, hydrological unit, and soilscapes were found to explain 75% of the variance in MAOM and POM, and 50% in the C/N ratio. The modified regressor chain indicated a nonlinear relationship between the C/N ratio and SOM due to the different decomposition rates of SOM as a result of variety in its nutrient quality. These spatial predictions enhance the understanding of soil properties’ distribution in Germany.

Robust-mv-M-LSTM-CI: Robust Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic

The digitalization of the global landscape of electricity consumption, combined with the impact of the pandemic and the implementation of lockdown measures, has required the development of a precise forecast of energy consumption to optimize the management of energy resources, particularly in pandemic contexts. To address this, this research introduces a novel forecasting model, the robust multivariate multilayered long- and short-term memory model with knowledge injection (Robust-mv-M-LSTM-CI), to improve the accuracy of forecasting models under uncertain conditions. This innovative model extends the capabilities of mv-M-LSTM-CI by incorporating an additional branch to extract energy consumption from adversarial noise. The experiment results show that Robust-mv-M-LSTM-CI demonstrates substantial improvements over mv-M-LSTM-CI and other models with adversarial training: multivariate multilayered long short-term memory (adv-M-LSTM), long short-term memory (adv-LSTM), bidirectional long short-term memory (adv-Bi-LSTM), and linear regression (adv-LR). The maximum noise level from the adversarial examples is 0.005. On average, across three datasets, the proposed model improves about 24.01% in mean percentage absolute error (MPAE), 18.43% in normalized root mean square error (NRMSE), and 8.53% in R2 over mv-M-LSTM-CI. In addition, the proposed model outperforms “adv-” models with MPAE improvements ranging from 35.74% to 89.80% across the datasets. In terms of NRMSE, improvements range from 36.76% to 80.00%. Furthermore, Robust-mv-M-LSTM-CI achieves remarkable improvements in the R2 score, ranging from 17.35% to 119.63%. The results indicate that the proposed model enhances overall accuracy while effectively mitigating the potential reduction in accuracy often associated with adversarial training models. By incorporating adversarial noise and COVID-19 case data, the proposed model demonstrates improved accuracy and robustness in forecasting energy consumption under uncertain conditions. This enhanced predictive capability will enable energy managers and policymakers to better anticipate and respond to fluctuations in energy demand during pandemics, ensuring more resilient and efficient energy systems.

Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season

Increasing air pollution has necessitated the prediction of pollutants over time. Deterministic, statistical, and machine-learning methods have been adopted to predict and forecast pollutant levels. It aids in planning and adopting measures to overcome the adverse effects of air pollution. This study employs long short-term memory (LSTM). This study used the hourly data from a meteorological station in a low-town area, Mohammedia City, Morocco. The model prediction accuracy was evaluated based on hourly, weekly, and seasonal (summer and winter) readings for the summer and winter of 2019, 2020 and 2021. Root mean square error (RMSE), mean absolute error (MAE) and mean arctangent absolute percentage error (MAAPE) were calculated to evaluate the accuracy of the developed LSTM model. The MAE value of 0.026 was observed to be less in winter than 0.029 during summer in 2019. Also, it was observed that MAE values decreased from Year 2019-2021, indicating increased prediction accuracy. MAAPE also observed a similar trend. However, RMSE values indicated the opposite for 2019 and 2020; in 2021, the RMSE value was 0.21 for summer and 0.14 for winter for hourly readings. Based on the error calculation, the study found weekly hourly readings were the most accurate for predicting SO2 concentration. Also, the LSTM model was more accurate in predicting winter SO2 concentration than in the summer season. Further studies must incorporate local incidences affecting the SO2 concentration into the LSTM model to increase its accuracy.

COMPARISON OF FORECASTING DRUG NEEDS USING TIME SERIES METHODS IN HEALTHCARE FACILITIES: A SYSTEMATIC REVIEW

Drug planning is essential to ensure the fulfillment of the right type, amount, and time criteria. Forecasting can be utilized during the planning stage to predict future drug needs. Perfect forecasting is impossible due to uncertainties in various factors, necessitating selecting the best method. This study aimed to identify the optimal forecasting method for healthcare facilities based on the smallest Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE) values obtained from forecasting results using time series methods like Single Moving Average (SMA), Weight Moving Average (WMA), (Single Exponential Smoothing) SES, Double Exponential Smoothing (DES), and Triple Exponential Smoothing (TES). This research involved a descriptive observational study with retrospective data and adhered to PRISMA guidelines. PubMed, Google Scholar, and Garuda served as the data sources. Nine articles meeting the eligibility criteria were employed. The findings revealed that the SES, DES, and TES methods produced forecasts with MAPE values below 10%, indicating highly accurate forecasting. The MAPE values for the SMA and WMA methods were less than 50%, which is still acceptable. Therefore, the ES methods, particularly SES, are highly recommended for accurate drug planning. Forecasting accuracy factors include data stability, pattern consistency, and smoothing constants. The SES method emerged as the best forecasting method, generating the smallest MAD, MSE, and MAPE values compared to other methods, falling below 10%, reflecting highly accurate forecasting.

Investigation of Taylor bubble behavior in upward and downward vertical and inclined pipe flows

The Taylor bubble behavior in two-phase flow is critical for various engineering applications, including momentum, heat, and mass transfer efficiencies. Therefore, understanding the dynamics of Taylor bubbles is crucial for the optimal design, operation, and safety of reactors and pipelines. In slug flow in pipes, the successive translation of liquid slugs and Taylor bubbles is characterized by the Taylor bubble velocity, which is a function of the flow distribution coefficient (C0). This study aims to compile a large database of Taylor bubble velocity and flow distribution coefficient in upward, horizontal, and downward flows, evaluate existing models, and propose a unified C0 model. As a result, it is found that the physics governing the flow distribution coefficient in downward flow is drastically different from that in upward flow, and it goes through two flow transitions, which are governed by the pipe inclination angle. This behavior is modeled by the proposed model, which predicts the Taylor bubble flow distribution coefficient through both transitions and captures its physical behavior in the downward flow. A validation study of the proposed model revealed that the proposed model outperforms the existing models as it has an average absolute percent error and standard deviation of 4.2% and 6.2%, respectively.

A coarse-to-fine ensemble method for capacity prediction of lithium-ion batteries in production

ABSTRACT The grading process is a critical stage in the production of lithium-ion batteries. Measuring capacity by full discharge is time-consuming and energy-intensive. Therefore, accurate and efficient capacity prediction is essential. However, current methods still require improvements in data selection, prediction accuracy and handling of dirty data. This paper proposes a coarse-to-fine ensemble learning framework using LightGBM regression algorithm to predict battery capacity. The framework uses raw statistical data directly, instead of complicated features extraction. From different perspectives of the whole data and classified data, the capacity is predicted from coarse to fine, and the semi-dirty data is predicted separately. The method is verified using lithium-ion battery datasets collected from actual industrial production lines. The experimental results show that the mean absolute error and mean absolute percentage error on the test set are 0.31 Ah and 0.11%, respectively, outperforming other regression prediction algorithms. Additionally, semi-dirty data, which accounts for 0.26% of the dataset, is predicted effectively.

Data-driven axial bearing capacity analysis of steel tubes infilled with rubberized alkali-activated concrete

This study aims to employ machine learning algorithms to analyze the axial bearing capacity of rubberized alkali-activated concrete filled steel tubes. A dataset encompassing 327 synthesized instances and seven input features is adopted for training and testing six machine learning models, including Decision Tree, Random Forest, Extremely Randomized Trees, Adaptive Boosting, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting Trees (XGBoost). The SHapley Additive exPlanation algorithm is employed to elucidate the prediction process of machine learning models and to analyze the influence of each parameter on axial bearing capacity. Comparison of evaluating metrics shows that GBDT and XGBoost models achieve highest accuracy and generalization capabilities when their Coefficient of Determination values surpassing 0.98 and Mean Absolute Percent Error remaining below 3%. Moreover, the explanation analysis of machine learning models reveals that diameter/width of the cross section, rubber content, yielding strength and thickness of steel tubes are critical variables that affect the axial bearing capacity, while compressive strength of alkali-activated concrete, specimen height, and shape of cross section show negligible impact. Besides, GBDT model overemphasizes the effect of specimen height and might lead a conservative prediction for specimens with smaller heights. Finally, compressive strength of alkali-activated concrete and diameter/width, thickness, and yielding strength of steel tubes are positively correlated with axial bearing capacity, and the increase of rubber content in alkali-activated concrete leads to the decrease of capacity.

A Hybrid Deep Learning Model to Estimate the Future Electricity Demand of Sustainable Cities

Rapid population growth, economic growth, and technological developments in recent years have led to a significant increase in electricity consumption. Therefore, the estimation of electrical energy demand is crucial for the planning of electricity generation and consumption in cities. This study proposes a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) techniques, both of which are deep learning techniques, to estimate electrical load demand. A hybrid deep learning model and LSTM model were applied to a dataset containing hourly electricity consumption and meteorological information of a city in Türkiye from 2017 to 2021. The results were evaluated using mean absolute percent error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics. The proposed CNN-LSTM hybrid model was compared to the LSTM model, with lower MAPE, MAE, and RMSE values. Furthermore, the CNN-LSTM model exhibited superior prediction performance with an R2 value of 0.8599 compared to the LSTM model with an R2 value of 0.8086. These results demonstrate the effectiveness of the proposed deep learning model in accurately estimating future electrical load demand to plan electricity generation for sustainable cities.

An approach for strength development assessment of cement-stabilized soils with various sand and fine contents

Cement stabilization is a well-established ground improvement technique. However, there have been limited investigations aimed at the effect of heterogeneous soil types with varying amounts of coarse-grained (sand) and fine-grained (silt and clay) substances, on the strength enhancement of cement-stabilized soils. In this cement stabilization research study, sand (S) and clayey silt (F) were mixed together to have a variety between coarse- and fine-grained fractions at 100, 75, 50, 25, and 0 % by dry weight, designated as the ratio of S:F=100:0, 75:25, 50:50, 25:75, and 0:100, respectively. The water content was prepared at the range of 1.25–2.50 optimal water content to simulate field applications. The strength enhancement of cement-stabilized soils was influenced by the fine content, water content, and cement content. The soil–water to cement ratio (s-w/c) was effectively incorporate the impact of both water and cement contents on the strength enhancement, for a given a specific fine content. The generalized correlation between unconfined compressive strength, qu and s-w/c could be represented as a power function: qu = M/(s-w/c)N. In this equation, M and N are constants that are primarily influenced by the fine content. The shear strength ratio versus fine content chart was proposed as a means to assess the effect of fine content on the strength of cement-stabilized soil with varying s-w/c. A stepwise approach for assessing the strength enhancement of cement stabilization in soil based on physical characteristics (i.e., plasticity index and fine content) was proposed and validated. The robustness of the proposed approach was realized by the low mean absolute percent error, (MAPE<7.0 %) and high coefficient of determination (R2 > 0.95) for measured and predicted strengths comparison. This technique serves as a valuable tool for mix design, specifically in relation to clay mineral and fine content. It supports in making engineering decision regarding the appropriate amount of water and cement needed to achieve strength requirements throughout the requisite curing period, while minimizing the number of repetitions needed.

Optimal Minimum-Velocity Threshold to Predict One-repetition Maximum for the Back Squat.

The prediction of one-repetition maximum (1RM) is highly relevant for strength and conditioning. The optimal minimum-velocity threshold (MVT) was recently proposed to increase the accuracy of 1RM predictions. Individual load-velocity profiles (LVP) were obtained in 18 athletes enrolled in recreational soccer. Reliability analyses were computed for all LVP-derived variables. Estimations of 1RM were made based on general (0.3 m.s- 1), pre-individual (mean velocity at 1RM obtained in a preliminary session) and optimal MVT (velocity that eliminates the difference between actual and predicted 1RM, determined in a preliminary session). The accuracy of 1RM predictions was examined using absolute-percent error and Bland-Altman plots. Between-day reliability of the LVP and 1RM was good (intraclass-correlation coefficients - ICCs>0.9 and coefficients of variation - CVs<5%). The individual and optimal MVT reached moderate-to-good reliability (ICCs>0.9 and CVs<10%, respectively). The predictions based on the optimal MVT displayed greater accuracy than those obtained with the individual and general MVT (absolute percent error: 2.8 vs. 5.5 vs. 4.9%, respectively). However, wide limits of agreement (LoA) were found between actual and estimated 1RM using this approach (~15 kg). Data indicate that the optimal MVT provides better estimations of 1RM for the free-weight back squat than the general and the individual MVT.

Predictive modeling of spring-back in pre-punched sheet roll forming using machine learning

This study outlines an experimental and computational endeavor aimed at developing a machine learning model to estimate spring-back values utilizing the decision tree methodology. A design of experiment approach was employed to collect a dataset, and based on the experimental results, a precise model was constructed to predict spring-back values. The model considered parameters such as thickness, diameter of circle hole, distance between the center hole and flange edge, and hole spacing. Various hyper parameters, including max depth and minimum samples for split, were explored, with configurations such as (30,5), (20,8), and (10,2) being evaluated to identify the optimal model for spring-back prediction. Analysis of the results demonstrated that the decision tree models accurately estimated spring-back values in cold roll forming of pre-punched sheets based on the input parameters. The coefficient of determination in the test section for decision tree models with parameters (30,5), (20,8), and (10,2) was found to be 0.90, 0.98, and 0.96, respectively. Additionally, the percentage of absolute error in the test section for the same decision tree models was calculated as 8.84%, 6.18%, and 7.6%, respectively.

A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis Differential Optical Absorption Spectroscopy measurements

The vertical distribution of tropospheric ozone (O3) is crucial for understanding atmospheric physicochemical processes. A Convolutional Neural Networks (CNN) method for the retrieval of tropospheric O3 vertical distribution from ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements to tackle the issue of stratospheric O3 absorption interference faced by MAX-DOAS in obtaining tropospheric O3 profiles. Firstly, a hybrid model, named PCA-F_Regression-SVR, is developed to screen features sensitive to O3 inversion based on the MAX-DOAS spectra and EAC4 reanalysis O3 profiles, which incorporates Principal Component Analysis (PCA), F_Regression function, and Support Vector Regression (SVR) algorithm. Thus, these screened features for ancillary inversion include the profiles of temperature, specific humidity, fraction of cloud coverage, eastward and northward wind, the profiles of SO2, NO2, and HCHO, as well as season and time features to serve as sensitive factors. Secondly, the preprocessed MAX-DOAS spectra dataset and the sensitive factor dataset are utilized as input, while the O3 profiles of the EAC4 reanalysis dataset incorporating the surface O3 concentrations are employed as output for constructing the CNN model. The Mean Absolute Percentage Error (MAPE) decreases from 26 % to approximately 19 %. Finally, the CNN model is applied for inversion and comparison of tropospheric O3 profiles using independent input data. The CNN model effectively reproduces the O3 profiles of the EAC4 dataset, showing a Gaussian-like spatial distribution with peaks primarily around 950 hPa (550 m). Since the reanalysis data used for model training has been smoothed, the CNN model is insensitive to extreme values. This behavior can be attributed to the MAPE loss function, which evaluates Absolute Percentage Errors (APEs) of O₃ concentration at all altitudes, resulting in varying retrieval accuracy across different altitudes while maintaining overall MAPE control. Temporally, the CNN model tends to overestimate surface O3 in summer by around 20 μg/m3, primarily due to the influence of the temperature feature in the sensitivity factor dataset. In conclusion, leveraging MAX-DOAS spectra enables the retrieval of tropospheric O3 vertical distribution through the established CNN model.

Compressive behavior of sustainable rubberized concrete‐filled steel tube columns having various recycled tire rubber aggregate contents and developing a predictive design model

AbstractAs a sustainable solution, employing recycled tire rubber aggregates in the production of concrete is of considerable interest. However, the applicability of rubberized concretes in construction is restricted due to their relatively low mechanical performance. Using rubberized concrete as a filling material in a steel tube provides a suitable solution for overcoming this issue and for using it effectively as a load‐bearing element. The objective of this study is to examine the impact of the material characteristics of the sustainable rubberized concrete‐filled steel tube (Ru‐CFST) and develop a design model based on ultimate strength prediction for the axially loaded Ru‐CFST stub columns with varying contents of tire rubber aggregate. The model was developed through the gene expression programming (GEP) technique by employing the experimental test results to determine the ultimate compressive strength of Ru‐CFST columns. Based on the principal component analysis, the sectional properties of the load‐bearing element (such as outer diameter, thickness, and length), material strengths (such as rubberized concrete and steel tube strengths), and rubber content were determined as statistically significant parameters affecting the ultimate axial strength of Ru‐CFST stub columns and thus, they were considered in the stage of the model generation. Furthermore, the proposed model's performance was compared to that of available models suggested for traditional CFST columns by commonly employed design codes, namely, ACI, AIJ, AISC, CSA, EC4, and GB. Based on the statistical analysis of the results, it can be concluded that the existing formulations have a relatively acceptable randomness and scatter of observations within the distribution, while the proposed design model gives a more accurate prediction for the ultimate bearing capacity of axially loaded Ru‐CFST columns with the highest R‐squared value of about 0.99 and the comparatively lowest mean absolute percent error of 7.54. It can be stated that the proposed GEP‐based design model will encourage engineers to use such sustainable structural members in their designs and constructions.