Regression Relationship Research Articles

Different Profiles of Jumper's Knee Indications in Basketball, Volleyball, and Football Athletes

PURPOSE: The present study aimed to compare the incidence of “jumper’s knee” injuries among athletes in basketball, volleyball, and football; to understand the causality and effect of the participation period and the incidence of jumper’s knee; to ascertain the link between history of injury and the incidence of jumper’s knee; and to determine the relationship between the type of tournament and the probability of jumper’s knee injury occurring.METHODS: This analytic, observational, cross-sectional study included subjects from high schools and clubs in Bandung, Solo, and Jogja, with a sample of approximately 30 participants representing each sport selected using a purposive sampling method (N = 93). Analytic observational using a cross sectional study approach in high schools and clubs in Bandung, Solo, and Jogja with a total sample of 30 for each sport selected by the purposive sampling method and obtained a total of 93 respondents. Dependent variable in this study is the jumper’s knee diagnosis. Statistical test data analysis used is different profiles using chi-square test effect of type of injury using binary regression relationship between participation period with jumper’s knee using Kendall's tau_b, the effect of playing time using binary regression and the relationship between the type of tournament and jumper’s knee using Kendall's tau_b. Statistical analysis of the dependent variable risk/indication for jumper’s knee used different methods. The chi-squared test was used to analyze the effect of type of injury using a binary regression method (Kendall's tau_b) to determine the relationship between participation period and jumper’s knee; and binary regression was used to assess the effect of playing time and the relationship between the type of tournament and jumper’s knee.RESULTS: Chi-squared analysis yielded an asymptotic significance value of p = .079, indicating no significant difference in the incidence of knee injuries among basketball, volleyball, and football athletes. Jumper's knee had positive percentage values of 38%, 34.6%, and 26.9% in basketball, volleyball, and football, respectively. Statistical tests examining the association between injury history and the incidence of jumper’s knee yielded a value of p = .00 with exp (b) = 0.075, which suggests an influence between ankle injury and jumper’s knee among the highest-risk athletes.CONCLUSIONS: Basketball, volleyball, and football players sustained jumper’s knee injuries at similar rates.

Analysis and Enlightenment on the Relationships between Two Kinds of Cutter Life Evaluation Indexes and Installation Radius: A Case Study

Accurate evaluation of cutter life at different cutter positions on the cutter head is helpful to determine the time of cutter change and reduce the time of cutter wear measurement, which is of great significance to improve the tunneling efficiency of tunnel boring machine (TBM) projects. Unfortunately, there is no unified cutter life evaluation index now. The field data of cutter wear are collected from a section of a long TBM tunneling water conveyance tunnel in China. Two kinds of cutter life evaluation indexes (based on the radial wear extent of cutter rings and replacement number of cutter rings) are selected and the variation rule between these two kinds of indexes with cutter installation radius is statistically analyzed. The results show that the regression relationships between the two kinds of cutter life evaluation indexes and installation radius mainly present linear functions and quadratic functions. Those regression relationships are affected by factors such as wear type, installation angle, cutter spacing, influence width, and allowable limit wear extent of cutter rings. Considering the calculation accuracy of the evaluation index, the actual working conditions of the disc cutter, and ignoring the influence of tunnel diameter, it is recommended to preferentially choose the radial wear extent of cutter rings per unit rolling distance as the evaluation index of cutter life. The research results can provide a reference for the selection of cutter life evaluation index, prediction of disc cutter life at different cutter positions, and establishment of cutter life prediction mode.

Exogenous indole-3-acetic acid promotes the plant growth and accumulation of selenium in grapevine under selenium stress

To alleviate the selenium (Se) stress in fruit trees and improve its accumulation, the effects of exogenous indole-3-acetic acid (IAA) on the growth and Se accumulation of grapevine under Se stress were studied. The application of exogenous IAA increased the biomass of grapevine, and the concentration of exogenous IAA had a regression relationship with the biomass. The root and shoot biomass were the maximum at 60 mg L− 1 IAA, increasing by 15.61% and 23.95%, respectively, compared with the control. Exogenous IAA also increased the photosynthetic pigments and the activities of superoxide dismutase and peroxidase in grapevine. Moreover, exogenous IAA increased the contents of total Se, organic Se, and inorganic Se, and the concentration of exogenous IAA had a regression relationship with the total Se content. The highest contents of root total Se and shoot total Se were accumulated at 90 mg L− 1 IAA, increasing by 29.94% and 55.77% respectively,. In addition, the correlation and path analyses revealed that the carotenoid content and root total Se content were closely associated with the shoot total Se content. Therefore, the application of exogenous IAA can alleviate the stress of Se to grape and promote its uptake and the most effective amount for the uptake of Se is 90 mg L− 1 IAA.

Research on Assimilation of Unmanned Aerial Vehicle Remote Sensing Data and AquaCrop Model.

Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R2 of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R2 and RMSE were 0.927 and 0.12, respectively. The canopy coverage R2 of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing.

Systematic Quantitative Analysis of Fetal Dexamethasone Exposure and Fetal Lung Maturation in Pregnant Animals: Model Informed Dexamethasone Precision Dose Study.

Dexamethasone (DEX) was applied in neonatal respiratory distress syndrome treatment of pregnant women. We established a pharmacokinetics (PK)/pharmacodynamics(PD)/end point model of pregnant animals based on published data and then extrapolated to simulate fetal exposure and lung maturation in pregnant women. We first established the PK/PD/end point model for DEX in pregnant sheep. We considered the competitive effect of cortisol (Cort) and DEX binding with glucocorticoid receptor and then used the indirect response model to describe disaturated-phosphatidylcholine (DSPC) dynamics. Based on that, we established a regression relationship between DSPC and fetal lung volume (V40). We then extrapolated the PD/end point model of pregnant sheep to pregnant monkeys by corrected stages of morphologic lung maturation in two species. Finally, we utilized the interspecies extrapolation strategy to simulate fetal exposure (AUC0-48h) and V40 relationship in pregnant women. The current model could well describe the maternal-fetal PK of DEX in pregnant animals. Simulated DEX AUC0-24h values of the umbilical venous to maternal plasma ratio in pregnant sheep and monkeys were 0.31 and 0.27, respectively. The simulated Cort curve and V40 in pregnant sheep closely matched the observed data within a 2-fold range. For pregnant monkeys, model-simulated V40 were well fitted with external verification data, which showed good interspecies extrapolation performance. Finally, we simulated fetal exposure-response relationship in pregnant women, which indicated that the fetal AUC0-48h of DEX should not be less than 300 and 100 ng/mL·hr at GW28 and GW34 to ensure fetal lung maturity. The current model preliminarily provided support for clinical DEX dose optimization.

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.

Relationship between otolith morphometry and fish total length of Chelon auratus from two localities (Mediterranean Sea and Ain Ziana Lake) in Libyan waters

ABSTRACT The relationship between fish total length and otolith length, width, and weight were inspected in the golden grey mullet, Chelon auratus, collected from the Mediterranean Sea coast of Libya and Ain Ziana, an inland lagoon located northeast of the Libyan coast. Chelon auratus is considered a valuable marketable fish in Libya. The main aim of the study was to estimate the statistical relationship between fish total length and the otolith length, width, and weight. This information will be noteworthy in the upcoming analyses on the biology of this species, which will provide critical information about feeding, age and growth of this species. Linear and non-linear regression models were used. The regression equations for each locality were significantly different, with exception of the right and left weight of individuals collected from the Mediterranean Sea coast as well as for the Ain Ziana Lagoon. The regression relationships were more homogeneous in Ain Ziana Lagoon than in the Mediterranean Sea, where there was greater variation in total length and otolith characteristics. The results obtained from this investigation were the first to address the relationship between otolith morphometric and total length for golden grey mullets collected from two localities in the Libyan waters.

Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements

Long-term preventive maintenance planning using finite annual budgets is vital for maintaining the service performance of airport runway composite pavements. Using the pavement condition index (PCI) to quantify composite pavement performance, this study investigated the PCI deterioration tendencies of middle runways, terminal runways, and taxiways and developed prediction models related to structural thickness and air traffic. Performance jump (PJ) and deterioration rate reduction (DRR) were used to measure maintenance benefits. Based on 112 composite pavement sections in the Long-term Pavement Performance Program, this study analyzed the influences of five typical preventive maintenance technologies on PJ, DRR, and PCI deterioration rates. The logarithmic regression relationship between PJ and PCI was obtained. For sections treated with crack sealing and crack filling, the DRR was nearly 0. For sections treated with fog seal, thin HMA overlay, and hot-mix recycled AC, the DRR was 0.2, 0.7, and 0.8, respectively. To solve the multi-objective maintenance problem, this study proposed a decision-making optimization method based on dynamic programming, and the solution algorithm was optimized, which was applied in a five-year maintenance plan. Considering different PCI deterioration tendencies of airport regions, as well as PJ, DRR, and costs of maintenance technologies, the preventive maintenance decision-making optimization method meets performance and financial requirements sufficiently.

Studying and enhancing the hypoglycemic effect of Pingyang Yellow Soup and preliminary exploration of its internal mechanisms in a zebrafish model

Yellow tea belongs to specialty tea, and it is loved by consumers because of its unique flavor and taste. Herein, Pingyang Yellow Soup (PYS, a kind of yellow tea) was chosen. In this study, we investigated the effects of PYS on the hypoglycemic effect of the zebrafish model and explored its potential mechanism of action. Results showed that PYS had very strong and definite hypoglycemic biological activity. Meanwhile, different water/material ratios, extraction temperatures, and extraction times had significant influence on the hypoglycemic performance of PYS, and the strength of influence was ranked as extraction temperature > extraction time > water/material ratio. In addition, results showed that the removal rate of tea polyphenols (TPs) in PYS had a strong linear correlation to the degree of decreased hypoglycemic performance, indicating the hypoglycemic performance was closely related to TPs levels. The comprehensive analysis revealed that the nor-sugar rate of PYS had a variety of regression relationships with its components, and the best hypoglycemic performance does not require the highest content levels of each component but depends on what it is, and there exists a relative optimal value, which provides basic information for exploring the internal mechanism of action of hypoglycemic effects and developing functional tea drinks of PYS.

Performances of different yield-detrending methods in assessing the impacts of agricultural drought and flooding: A case study in the middle-and-lower reach of the Yangtze River, China

In evaluating the impacts of drought and flooding disasters on crop yields, accurately calculating meteorological yield (i.e., detrended yield) is an important procedure. The present work aimed to compare various yield-detrending methods in terms of characterizing the regression relationships between meteorological yield and the intensities of drought and flooding. Taking the middle-and-lower reach of the Yangtze River (MLRYR) as the study region, the intensities of drought and flooding during the growing seasons of four study crops (cotton, oilseed rape, wheat, and maize) were quantified using the standardized precipitation evapotranspiration index. Nine popular yield-detrending methods and the first-difference method were employed to determine meteorological yields. The results indicated that the examined methods consistently identified the cases with very significant yield-reducing impacts of drought and flooding. The first-difference method identified 58 significant regression relationships, outperforming the yield-detrending methods (identifying 39–44 significant regression relationships). The 20-yr moving average and the linear fitting methods performed best at the provincial level, while the cubic smoothing spline and the quadratic polynomial fitting methods performed best at the district level. Based on these best-performing methods, the average method was proposed and exhibited wider applicability and better performance than the individual methods. In terms of meteorological yield losses, cotton was the most affected crop (35% of all districts experienced severe cotton yield loss) and Anhui was the most affected region (with an average crop yield loss of 14.06%). The yield-reducing impact of flooding was significant in 41 districts; in comparison, the impact of drought was significant only in 7 districts. Additionally, oilseed rape was the crop most affected by drought and flooding. These results can provide guidance for assessing agricultural drought and flooding disasters under climate change.

Analysis of Ground Vibration Levels Due to the Blasting Process at PT. Bumi Suksesindo

Ground vibration is one of the effects of the blasting process; when the ground vibration reaches the highest level, it will disturb comfort and even cause damage to the surrounding building structure. This research aims to determine the magnitude of ground vibrations in Pit A and Pit C, as well as determine the relationship between Peak Particle Velocity (PPV) and scaled Distance, and determine the maximum explosive charge weight per delay based on the SNI 7571: 2010 reference. Actual ground vibration measurement data during research based on PPV theory and the actual PPV power regression relationship with scaled distance was used to obtain a ground vibration prediction formula to be a reference for determining the amount of explosive filling per delay. The ground vibration produced in the blasting process is hoped not to exceed the safe threshold. Prediction of the ground vibration formula at 100 m to 1500 m according to the US Bureau of Mines where the Mean Squared Error (MSE) value is 0.54, the MSE value from the Langefors-Kihlstrom equation is 1.85 while the MSE value from the Ambersays-Hendorn equation is 0.31 with the slightest deviation is very good to use as a reference for predicting ground vibrations with the predicted PPV formula. Hence, the maximum explosive charge with a PPV limit of 2 mm/s is 2.452 kg, a PPV limit of 3 mm/s is 11.332 kg, and a PPV limit of 5 mm/s is 23.040 kg. The factors that influence ground vibration are the Distance from the blasting location to the measurement location and the maximum number of explosives per delay, so the results taken from this research are that blasting in Pit A and Pit C is still categorized as safe for infrastructure and community housing.

HPTLC Stability Indicating Analytical Method of Andrographolide and 5-fluorouracil with Network Pharmacology Analysis against Cancer.

Herbal drugs when used in combination with chemotherapeutic drugs can reduce the side effects and increase the efficacy by acting on multiple targets. Andrographolide (AG), a diterpene lactone isolated from Andrographis paniculata Nees, is a bioactive compound with anticancer potential, and 5-fluorouracil (FU), a pyrimidine analogue, is used in the treatment of cancer. Both drugs are used to formulate combination nanoformulation to increase absorption, thereby increasing their oral bioavailability. The study aimed to develop and validate stability indicating simultaneous HPTLC method for quantification of FU and AG in combination nanoformulation along with in silico docking and network pharmacology analysis to understand the interaction between the drugs and cancer targets. Chromatographic separation was performed using mobile phase chloroform: methanol: formic acid (9: 0.5: 0.5, v/v/v) on HPTLC silica plates 60 F254 as a stationary phase using UV-Vis detector and HPTLC scanner at 254 nm. Further, in silico docking analysis was performed to predict the binding affinity of AG and FU with different proteins and network pharmacology to find out the exact biomolecular relationship of AG and FU in alleviating cancer. The data from the calibration curve showed a good linear regression relationship with r² = 0.9981 (FU) and r² = 0.9977 (AG) in the concentration range of 0.1-2.0 μg/mL. The developed method was validated according to the ICH guidelines. Stability studies showed changes in peak patterns and areas. Bioinformatic and network pharmacology analyses of AG and FU with target proteins and genes associated with cancer play a multimechanistic role in alleviating cancer. The developed method has been concluded to be robust, simple, precise, reproducible, accurate, and stability indicating for simultaneous quantification of AG and FU, and the molecular interaction studies have further indicated that the combination nanoformulation of AG and FU could be effective against cancer.

Piezometric depth modeling of groundwater using monthly variables of precipitation and water consumption (case study: Sarab Plain aquifer)

The growth of the world population and the problem of food supply have led to the development of agricultural land, particularly in the Third World and in Iran, and thus to a sharp increase in water consumption regardless of the existing water resources. On the other hand, the ever-increasing growth of industries and factories, regardless of the impact on the environment, together with the increase in water consumption, has disturbed the balance of the environment and caused climate change with rising temperatures and increasing pollution. Unfortunately, the management of water resources and the environment is incompatible with the development of agricultural land and the development of industries, and therefore in most countries of the world a situation has arisen in which surface and groundwater resources are at risk. The two main variables, precipitation and water consumption, control groundwater levels. The area studied in this research is the Sarab Plain aquifer located in East Azerbaijan province, Iran. In the Sarab Plain and other plains of Iran, indiscriminate harvesting has led to a significant decline in the groundwater level (in other words, piezometric depth) and subsidence of the plain. The area under cultivation of various agricultural crops such as beans, cucumbers and alfalfa and gardens is about 38,176 ha, irrigated by 739 licensed wells. Agricultural uses on the one hand and industrial and animal uses on the other led to a progressive lowering of the piezometric level of the plain. The average water consumption from the table is currently 53 million cubic meters per year, while the amount of renewable water is 35.81 million cubic meters per year. The data used in the study are monthly precipitation from 19 rain gauge stations, monthly piezometric codes from 78 piezometers converted to piezometric depth, and monthly water consumption from 1886 consumable wells between 2007 and 2022. Individual regression relationships were created between the piezometric depth variable and consumption and precipitation variables. In the first step, a general hybrid exponential relationship between piezometric depth, consumption and precipitation was found. The correlation coefficient value between the calculated and observed piezometric depth was 0.69. Furthermore, the root mean square error and Kling-Gupta were 2 m and 0.57, respectively. In order to apply the hybrid exponential relationship to predict piezometric depth in the coming years, it was necessary to predict precipitation and consumption. To predict monthly precipitation based on its periodicity, the Thomas and Fiering (T&F) consumption forecasting method was used. 20% of the data was compared with calculated data. The result showed, R = 0.815 and RMSE = 0.07 mm between calculated and observed data. Additionally, to predict consumption in the coming years, a suitable regression relationship between consumption and time was constructed, showing a correlation of 0.97 and a root mean square error of 0.0008 mcm with observations. In the second step, precipitation and consumption were predicted for the next 3 years (2023–2025) and piezometric depth were determined for this period by applying them in the hybrid model. The forecast for the next three years shows that the upward trend of the piezometric level will continue. The application of the regression method resulted in a final equation, which is particularly important in view of the stabilization of the piezometric level of the reservoir. This method has no particular limitations and is an appropriate method when accurate consumption water and precipitation statistics are available. The only limitation that can be considered with this method is the movement around the average values and does not take into account the positional fluctuations. This work is new because it calculates groundwater simultaneously using two parameters: precipitation and water consumption. Other similar studies did not use groundwater consumption data.

Errors in average strength estimates of sedimentary rock masses in South Yakutia based on geometric induction sounding data

The probabilistic model developed to describe the engineering and geological conditions for the operation of buildings and infrastructure in the city of Neryungri has been verified. The model is a power-function equation which correlates, by regression relationships, laboratory-determined compressive strength of saturated frozen sandstone samples with attenuation of the high-frequency harmonic field of a vertical magnetic dipole in warm permafrost composed of sandstone. The attenuation values were obtained by geometric electromagnetic induction sounding at a frequency of 1.125 MHz. The model verification was conducted at a fixed frequency on a section of the Amur-Yakutsk Railway located at a significant distance from Neryungri. The railway here is underlain by low-temperature dolomite masses. The statistical analysis indicates that the relative errors of the dolomite saturated strength estimates from the geometric EM induction data with a 73.0% probability do not exceed 20%. With this level of accuracy close to that of laboratory tests according to GOST 25100–2020, it is reasonable to suggest that the probabilistic model can be used region-wide in southern Yakutia to predict saturated strength of foundation materials composed of sandstone and dolomite. It can provide an effective solution for market-based construction and mining industries, as increasing anthropogenic and climatic impacts are anticipated to cause thawing and saturation of sedimentary rock masses.

Dietary fat and carbohydrate-balancing the lactation performance and methane emissions in the dairy cow industry: A meta-analysis

For the agroecosystems of the dairy cow industry, dietary carbohydrate (starch, neutral detergent fiber [NDF]) and fat could directly affect rumen methane emissions and host energy utilization. However, the relationships among diet, lactation performance, and methane emissions need to be further determined to assist dairy farms to adjust diet formulations and feeding strategies for environmental and production management. A meta-analysis was conducted in the current study to explore quantitative patterns of dietary fat and carbohydrate at different levels in balancing lactation performance and environment sustainability of dairy cows, and to establish a methane emission prediction model using the artificial neural network (ANN) model. The results showed that the regression relationship between dietary fat, carbohydrate and methane emissions could be shown by the following models: methane = 106.78 + (14.86 × DMI), R2 = 0.80; methane = 443.17 – (46.41 × starch/NDF), R2 = 0.76; and methane = 388.91 + (31.40 × fat) – (5.42 × fat2), R2 = 0.80. The regression relationships between dietary fat, carbohydrate and lactation performance could be shown by the following models: milk fat yield = 1.08 + (0.43 × starch/NDF) – [0.34 × (starch/NDF)2], R2 = 0.79; milk protein yield = 0.68 + (0.15 × fat) – (0.016 × fat2), R2 = 0.82. In the structural equation model, we found that when formulating dietary carbohydrates and fats, it was necessary to balance the relationship between methane emissions and lactation performance. Specifically, dietary starch/NDF was lower than 0.63 (extremum point) and dietary fat was between 2.89% and 4.69% (extremum point), it could ensure that the aim of methane emission reduction (methane emissions decrease with increasing dietary starch/NDF and fat) was achieved without losing lactation performance of dairy cows (lactation performance increase with increasing dietary starch/NDF and fat). Finally, we established the ANN model to predict methane emissions (training set: R2 = 0.62; validation set: R2 = 0.61).

Estimation of soil health in the semi‑arid regions of northwestern Iran using digital elevation model and remote sensing data.

Nowadays, neglecting soil conservation issues is one of the most critical factors in reducing soil health (SH). In this regard, to facilitate the estimation of the SH in northwestern Iran, 292 soil samples were taken from a depth of 0-30cm of this area, and a wide range of soil properties were determined. Then, soil health indices (SHIs) were calculated. Simultaneously, the normalized difference vegetation index (NDVI), surface water capacity index (SWCI), and a digital elevation model (DEM) were obtained from satellite data. Finally, multiple linear regression (MLR) relationships between these parameters and SHIs were calculated. In this study, there was a highest significant positive correlation (P < 0.01) between IHI-LTDS and SWCI (0.71**), DEM (0.76**), and NDVI (0.73**). The MLR, with both the whole total (TDS) and minimal (MDS) dataset methods, which includes the aforementioned indices, strongly described the spatial variability of the Integrated Soil Health Index (IHI) (R2 = 0.78, AIC = - 416, RMSE = 0.05, and ρc = 0.76). According to the results of this study, it can be said that the development of SH estimation models using remote sensing extracted parameters can be one of the effective ways to reduce the cost and time of soil sampling in extensive areas.

Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

The energy consumption, daylighting, and thermal comfort of buildings directly affect the three key goals of residents. However, there is little research on the optimization of energy consumption, daylighting, and thermal comfort in residential buildings in China. Therefore, this study proposes an optimization framework that combines Bayesian optimization with extreme gradient boosting trees (BO-XGBoost) and non-dominated genetic algorithm-II (NSGA-II) to study the multi-objective optimization of residential building performance. This paper first uses Grasshopper to simulate and obtain a dataset through Latin hypercube sampling (LHS). BO-XGBoost is used to establish the regression relationship between building envelope design parameters and residential building performance. Then, the obtained regression model is used as the fitness function of NSGA-II to get the Pareto optimal solution set. Finally, the ideal point method is used to obtain the optimal combination of building envelope structure parameters for residential buildings. Taking a residential building in a hot summer and cold winter area as an example, the effectiveness of this method is verified. The results show that (1) BO-XGBoost has excellent predictive performance, with R2 values of 0.997, 0.960, and 0.994 for energy consumption, thermal comfort, and daylighting, respectively. (2) The proposed BO-XGBoost-NSGA-II can effectively achieve multi-objective optimization. Compared with the initial scheme of the case building, energy consumption is reduced by 44.1%, thermal comfort index is reduced by 10.9%, and daylighting performance is improved by 1.7%. Therefore, the proposed method can effectively optimize the performance goals of residential buildings and provide practical ideas for similar problems.

Problems with reinforced concrete industrial floors with regard to subsoil swelling

Most of the problems associated with open cracks in reinforced concrete industrial floors do not arise from technological indiscipline in the execution or exceeding the permitted floor load, but from the geotechnical profile beneath the floor. In the presence of swelling soil in the subsoil, the floors can then be shifted upwards by centimeters and create open cracks. This article describes regression relationships for the prediction of swelling pressure and deformation of reinforced concrete industrial floors based on indirect measurements. These relationships were obtained by evaluating a large database of measurements carried out by the company GeoTec-GS and the Czech Technical University in Prague using neural networks, multiple correlation, regression analysis, and sensitivity analysis. The article also presents the actual classification of the risk of surface damage of reinforced concrete floors due to swelling of the subsoil and an example of its application is given.

A deep learning-based imputation method for missing gaps in satellite aerosol products by fusing numerical model data

Satellite-based aerosol optical depth (AOD) products are commonly used in various aerosol-related studies, such as aerosol pollution mapping and aerosol-climate interactions. However, these satellite AOD products often suffer from significant missing gaps due to cloud cover and limitations in the retrieval algorithm. To address this issue, some studies take advantage of real-time seamless simulation of numerical models and successfully fill in these gaps by establishing a regression relationship between satellite AOD and numerical model AOD. However, these previous studies usually use satellite AOD retrievals as the regression target, which limits the accuracy of the imputation results by the original accuracy of satellite AOD retrievals and also consumes a considerable amount of time. To overcome these limitations, this study proposes a spatiotemporal imputation model called Bi-ConvRNN, which combines convolutional neural networks (CNN) and bidirectional recurrent neural networks (Bi-RNN). The model takes both satellite AOD retrievals and numerical model AOD data as input and utilizes the weighted mean squared error (MSE) loss function of multiple AOD datasets, e.g., ground-based data, satellite retrievals, and numerical simulation, as the optimization target to improve the imputation accuracy. The proposed model is evaluated using hourly COMS GOCI AOD products. In the independent test set, the AOD results generated by the Bi-ConvRNN model in the region containing GOCI AOD retrievals can break the accuracy of original GOCI AOD products with the accuracy improved from R2 = 0.70 [RMSE = 0.15] to R2 = 0.84 [RMSE = 0.11], and the filling accuracy, e.g. R2 = 0.79, [RMSE = 0.14], in the region without GOCI AOD retrievals are still better than those of the original GOCI AOD retrievals. Additionally, the Bi-ConvRNN model demonstrates satisfactory filling efficiency, requiring only 0.12 s to fill in the missing gaps of hourly GOCI AOD products per day. These results highlight the efficiency and reliability of the proposed model in filling the gaps in satellite AOD products, and the filled AOD results have great potential for further aerosol-related research.

Integration of Artificial Intelligence Applications and Knowledge Management Processes for Construction Projects Management

Artificial intelligence systems have gained access to various scientific and research fields, especially in the construction industry. The study seeks to confirm the vital role of introducing Knowledge Management (KM) integrated with Artificial intelligence (AI) applications in the projects. It requires qualifying engineers and imposing their current qualifications to achieve the benefits of Integration of AI Applications based on KM processes to perform their professional roles and recognize the need to develop their capabilities through training and development. The field survey was intended only for 85 engineers working on construction projects (public and private sectors). Three axes were clarified to allocate the extent of the sample response and determine the benefits of using the KM process and AI applications for the success of construction projects. The results showed a positive relationship between the demographic variables of the response and the benefit of using the KM process and AI applications and explaining the variance in the regression relationships. Therefore, the study suggests integrating AI applications based on the KM process to achieve business goals and effectively benefit and exchange management, as its use leads to faster and more effective decision-making, especially if the project strategy approves it. Doi: 10.28991/CEJ-2024-010-03-06 Full Text: PDF