Search Results (111,390)

Climate change significantly impacts plant growth by reducing the availability of essential nutrients, including phosphorus (P). As an alternative to chemical fertilizers, climate-smart agriculture should prioritize the use of beneficial microorganisms such as P-solubilizing bacteria (PSB). Here, we report the ability of the P1.5S strain of Bacillus safensis to solubilize P under the stress caused by different pH, temperature, and salinity. Genomic data and the TBLASTN algorithm were used to identify genes involved in stress tolerance and P solubilization. Stress tolerance was confirmed by cultivation under varying conditions, while the mechanism of P solubilization was investigated using HPLC. Bioinformatic analysis revealed at least 99 genes related to stress tolerance, 32 genes responsible for organic acids synthesis, as well as 10 genes involved in phosphatase production. B. safensis P1.5S can grow at 37 °C, high NaCl concentrations (15 g/L), and is tolerant of alkaline and acidic conditions. The P1.5S strain primarily solubilizes P by releasing organic acids, including lactic, acetic, and succinic acid. Our data revealed that the efficacy of P solubilization was not affected by abiotic stressors (19.54 µg P/mL). By evaluating the P solubilization ability of B. safensis P1.5S induced by stressors represented by varying pH, temperature, and salinity conditions, this work introduces a new avenue for increasing P availability, which enables and endorses the future development of practical applications of B. safensis P1.5S in challenging agricultural environments. Full article

The current evaluation models for earth-rock dam compaction quality seldom incorporate parameter uncertainty considerations. Additionally, the existing models frequently demonstrate constrained prediction accuracy and generalization capabilities. To resolve these issues, we present an intelligent evaluation method for the compaction quality of earth-rock dams that explicitly accounts for parameter uncertainty. The method utilizes a dynamic inertia weight, an adaptive factor, and a differential evolution strategy to enhance the search capability of the firefly algorithm. Furthermore, the random forest (RF) algorithm’s Ntree and Mtry parameters are adaptively optimized through the improved firefly algorithm (IFA) to develop a dam compaction quality prediction model. This model aims to reveal the complex nonlinear mapping relationship between input influencing factors, such as compaction parameters, material source parameters, and meteorological factors, and the compaction quality. The proposed model improves the prediction accuracy, generalization ability, and robustness. The improved firefly optimization-based random forest (IFA-RF) is applied in practical engineering projects, and the results validate that this method can reliably and accurately predict the compaction quality of earth-rock dam construction in real time (R = 0.90107, MSE = 0.0000602, p = 0.000) and thereby guide remedial measures to ensure engineering safety and quality compliance. Full article

The conservation and monitoring of archaeological sites submerged in water reservoirs have become increasingly necessary in a climatic context where water management policies are possibly accelerating erosion and sedimentation processes. This study assesses the potential of using multitemporal LiDAR data and Machine Learning (ML)—specifically the XGBoost algorithm—to predict erosional and sedimentary processes affecting archaeological sites in the Valdecañas Reservoir (Spain). Using data from 2010 to 2023, topographic variations were calculated through a robust workflow that included the co-registration of LiDAR point clouds and the generation of high-resolution DEMs. Hydrological variables, topographic descriptors, and water dynamics-related factors were extracted and used to train models based on the detected measurement errors and the temporal ranges of the DEMs. The model trained with 2018–2023 data exhibited the highest predictive performance (R² = 0.685), suggesting that sedimentary and erosional patterns are partially predictable. Finally, a multicriteria approach was applied using a DEM generated from 1957 aerial photographs to estimate past variations based on historical terrain conditions. The results indicate that areas exposed to fluctuating water levels and different topographic orientations suffer greater damage. This study highlights the value of LiDAR and ML in assessing the vulnerability of archaeological sites in highly dynamic environments. Full article

The cultural and creative industries have garnered widespread attention as an important vehicle for promoting the transformation and upgrading of urban industrial structures. In this investigation, we take Harbin—a city in China with a strong industrial legacy—as a case study. Through kernel density analysis and the DBSCAN clustering algorithm, we identify and analyze the spatial distribution and spatiotemporal evolution patterns of 157 clusters of cultural and creative industries in Harbin. We construct a Geographic Weighted Regression (GWR) model using 20 indicators from three categories (i.e., Thank you for your comments. We have carefully reviewed this section to ensure that it is accurate.social, cultural, and economic) to analyze the factors impacting the agglomeration of cultural and creative industries in Harbin. Our findings reveal that the cultural and creative industries exhibit an agglomeration pattern. A large-scale agglomeration area for large enterprises has formed in the city center, while scattered, small-scale agglomeration zones for emerging small enterprises have formed in newly developed areas on the urban periphery. The GWR analysis indicates that economic factors have the most significant impact on the agglomeration of cultural and creative industries; however, night-time economic facilities show a negative correlation. Among social factors, the convenience of public transportation and new energy transportation infrastructure have a significant impact on industrial agglomeration. Regarding cultural factors, cultural and sports facilities have the greatest influence, while public information kiosks and public arts education facilities exhibit spatial variability. These findings provide a scientific basis for policy formulation and contribute to promoting the healthy development of cultural and creative industries. Full article

In order to address the problem that the paint surface of the damaged region of the body is similar to the color texture characteristics of the usual paint surface, which leads to the phenomenon of leakage or misdetection in the detection process, an algorithm for detecting the damaged region of the body based on the improved YOLOv11 is proposed. Firstly, bi-deformable convolution is proposed to optimize the convolution kernel shape offset direction, which effectively improves the feature representation power of the backbone network; secondly, the C2PSA-SCSA module is designed to construct the coupling between spatial attention and channel attention, which enhances the perceptual power of the backbone network, and makes the model pay better attention to the damaged region features. Then, based on the GSConv module and the DWConv module, we build the slim-neck feature fusion network based on the GSConv module and DWConv module, which effectively fuses local features and global features to improve the saturation of semantic features; finally, the Focaler-CIoU border loss function is designed, which makes use of the principle of Focaler-IoU segmented linear mapping, adjusts the border loss function’s attention to different samples, and improves the model’s convergence of feature learning at various scales. The experimental results show that the enhanced YOLOv11-BSS network improves the precision rate by 7.9%, the recall rate by 1.4%, and the mAP@50 by 3.7% over the baseline network, which effectively reduces the leakage and misdetection of the damaged areas of the car body. Full article

Rosin is a biomass-based chemical raw material employed in multiple industries: paper, polymers, coatings, adhesives, and more, while glycerol production has experienced a notable increment in recent decades due to it being an unavoidable by-product of the biodiesel industry. Rosin polyol esters are of high interest, and a potential route for the valorization of glycerol. In this work, we compare in detail the esterification routes of rosin triglycerides via classical, industrial thermal processes at 260–280 °C and similar processes catalyzed by Irganox 1425, a high-molecular-weight, multifunctional, phenolic, primary antioxidant produced by BASF and usually in rosin processes. Its chemical name is calcium bis(ethyl 3,5-di-tert-butyl-4-hydroxybenzylphosphonate). To this end, a novel RP-HPLC method provided us with a detailed description of the compositional evolution of the reacting media. These data have been the basis of a non-linear kinetic modeling procedure where we applied non-linear regression and numerical integration algorithms to determine the network of chemical reactions and the kinetic model of the rosin–glycerol esterification process. Furthermore, the comparison of such kinetic models and their parameters allows us to understand the kinetic effect of the addition of the homogeneous catalyst. The effect of Irganox 1425 results in a notable enhancement of the reaction rates, thus allowing for operation at lower temperatures and a reduction in side reactions as decarboxylation. Full article

Large reservoir-induced landslides pose a persistent threat to the safety of the Three Gorges Project and the Yangtze River shipping channel. A comprehensive multi-field monitoring system has been established to observe potential landslide areas within the Three Gorges Reservoir Area. The tasks of effectively utilizing these extensive datasets and exploring the underlying correlation among various monitoring objects have become critical for understanding landslide movement patterns, assessing stability, and informing disaster prevention measures. This study focuses on the No. 1 riverside sliding mass of the Huangtupo landslide, a representative large-scale landslide in the Three Gorges Area. We specifically analyze the deformation characteristics at multiple monitoring points on the landslide surface and within underground tunnels. The analysis reveals a progressive increase in deformation rates from the rear to the front and from west to east. Representative monitoring points were selected from the front, middle, and rear sections of the landslide, along with four hydrological factors, including two reservoir water factors and two rainfall factors. These datasets were classified using the K-means clustering algorithm, while the FP-Growth algorithm was employed to uncover correlations between landslide deformation and hydrological factors. The results indicate significant spatial variability in the impacts of reservoir water levels and rainfall on the sliding mass. Specifically, reservoir water levels influence the overall deformation of the landslide, with medium-to-low water levels (146.32 to 163.23 m) or drawdowns (−18.70 to −2.16 m/month) accelerating deformation, whereas high water levels (165.37 to 175.10 m) or rising water levels (4.45 to 17.33 m/month) tend to mitigate it. In contrast, rainfall has minimal effects on the front of the landslide but significantly impacts the middle and rear areas. Given that landslide deformation is primarily driven by periodic fluctuations in reservoir water levels at the front, the movement pattern of the landslide is identified as retrogressive. The association rules derived from this study were validated using field monitoring data, demonstrating that the data mining method, in contrast to traditional statistical methods, enables the faster and more intuitive identification of reservoir-induced landslide deformation patterns and underlying mechanisms within extensive datasets. Full article

Differentiating tumor progression from radionecrosis in patients with treated brain glioma represents a significant clinical challenge due to overlapping imaging features. This study aimed to develop and evaluate a machine learning model that integrates radiomics features and T2*-weighted Dynamic Susceptibility Contrast MRI perfusion (DSC MRI) parameters to improve diagnostic accuracy in distinguishing these entities. A retrospective cohort of 46 patients (25 with confirmed radionecrosis, 21 with glioma progression) was analyzed. From lesion segmentation on DSC MRI, 851 radiomics features were extracted using PyRadiomics, alongside seven perfusion parameters (e.g., relative cerebral blood volume, time to peak) obtained from time–intensity curves (TICs). These features were combined into a single dataset and 14 classification algorithms were evaluated with GroupKFold cross-validation (k = 4). The top-performing model was selected based on predictive area under the curve (AUC) yield. The Logistic Regression classifier achieved the highest performance, with an AUC of 0.88, followed by multilayer perceptron and AdaBoost with AUC values of 0.85 and 0.79, respectively. The precision values were 72%, 74%, and 78% for the three models, respectively, while the accuracy was 63%, 70%, and 71%. Key predictive variables included radiomics features like wavelet-HHH_firstorder_Mean and mean normalized TIC values. Our combined approach integrating radiomics and DSC MRI parameters shows strong potential for distinguishing radionecrosis from glioma progression. However, further validation with larger cohorts is essential to confirm the generalizability of this approach. Full article

The contribution of this study is in the novel application of the bin packing algorithm that is used to optimize the robotic bricklaying process with the aim of minimizing the wearing of a robotic saw used for splitting brick blocks so as to minimize brick consumption. To optimize the cutting of masonry blocks with a robotic saw, a new bin packing algorithm has been developed to enhance the design of a digital cutting plan. The algorithm is based on the principle of random search for all combinations of cutting execution with respect to the maximum number of objects (cuts) found in one container (masonry block). The new bin packing algorithm (NBPA) minimizes the number of total masonry blocks (containers) and the number of cuts made with a robotic saw, thus reducing the cutting length. The algorithm can converge to a solution rather quickly and reliably to identify optimal variants of a digital plan designed for a robotic saw to be used in different object assemblies. This article describes the optimization algorithm, including step-by-step calculations, and provides a practical example and a comparison of the results with earlier algorithms. The concept of the robotic saw is also presented in detail, including a description of a prototype. The simulation of the performance on 20 different sets of elements showed that NBPA has a similar use of space compared to the First-Fit Decreasing algorithm (FFD). Multicriteria analysis demonstrated that when the weighting criterion for saw wear was 40% of all the criteria, the use of NBPA was approximately 3.5 times more effective than FFD. The application of the new methodology to a robotic bricklaying process has the potential to reduce the wear of robotic saw, to increase the speed of the construction process and to reduce the generation of construction and demolition waste (CDW). Full article

The elastic modulus of basalt fibers is closely associated with their chemical composition. In this study, eight machine learning models were developed to predict the elastic modulus, with hyper-parameter tuning implemented through the GridSearchCV technique. Model performance was evaluated using the coefficient of determination (R²), root-mean-square error (RMSE), and mean absolute error (MAE). SHAP analysis was employed to uncover the relevance of oxide compositions and their interactions with the elastic modulus. Among these models, the Categorical Boosting algorithm exhibited the best results, with an R² of 0.9554, an RMSE of 4.7556, and an MAE of 2.0323. SHAP analysis indicated that CaO had the most significant influence on elastic modulus predictions. The importance of other oxides was ranked as follows: SiO₂, Al₂O₃, MgO, K₂O, Na₂O, Fe₂O₃, FeO, and TiO². Additionally, SHAP analysis determined oxide ranges for positive elastic modulus prediction. This research provides new insights into leveraging machine learning to optimize the mechanical properties of basalt fibers. Full article

Background: Primary and secondary salivary gland malignancies differ significantly in treatment and prognosis. However, conventional ultrasonography often struggles to differentiate between these malignancies due to overlapping imaging features. We aimed to develop and evaluate noninvasive diagnostic models based on traditional ultrasound features, radiomics, and deep learning—independently or in combination—for distinguishing between primary and secondary salivary gland malignancies. Methods: This retrospective study included a total of 140 patients, comprising 68 with primary and 72 with secondary salivary gland malignancies, all pathologically confirmed, from four medical centers. Ultrasound features of salivary gland tumors were analyzed, and a radiomics model was established. Transfer learning with multiple pre-trained models was used to create deep learning (DL) models from which features were extracted and combined with radiomics features to construct a radiomics-deep learning (RadiomicsDL) model. A combined model was further developed by integrating ultrasound features. Least absolute shrinkage and selection operator (LASSO) regression and various machine learning algorithms were employed for feature selection and modeling. The optimal model was determined based on the area under the receiver operating characteristic curve (AUC), and interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: The RadiomicsDL model, which combines radiomics and deep learning features using the Multi-Layer Perceptron (MLP), demonstrated the best performance on the test set with an AUC of 0.807. This surpassed the performances of the ultrasound (US), radiomics, DL, and combined models, which achieved AUCs of 0.421, 0.636, 0.763, and 0.711, respectively. SHAP analysis revealed that the radiomic feature Wavelet_LHH_glcm_SumEntropy contributed most significantly to the mode. Conclusions: The RadiomicsDL model based on ultrasound images provides an efficient and non-invasive method to differentiate between primary and secondary salivary gland malignancies. Full article

In this study, the particle swarm optimization (PSO) and back propagation neural network (BPNN) surrogate model in combination with a multi-objective genetic algorithm are developed for the design optimization of a bionic liquid cooling plate with a spider-web channel structure. The single-factor sensitivity analysis is first conducted based on the numerical simulation approach, identifying three key factors as design variables for optimizing design objectives such as maximum temperature (T_max), maximum temperature difference (ΔT_max), and pressure drop (ΔP). Subsequently, the PSO algorithm is used to optimize the parameters of the BPNN structure, thereby constructing the PSO-BPNN surrogate model. Next, the non-dominated sorting genetic algorithm II (NSGA-II) is employed to obtain the Pareto optimal set, and the TOPSIS with the entropy weight method is used to determine the optimal solution, eliminating subjective preferences in decision-making. The results show that the PSO-BPNN model outperforms the traditional BPNN in prediction accuracy for all three objectives. Compared to the initial structure, the T_max and ΔT_max are reduced by 1.09 °C and 0.41 °C in the optimized structure, respectively, with an increase in ΔP by 21.24 Pa. Full article

In the present paper, we prove a new local convergence theorem with initial conditions and error estimates that ensure the Q-quadratic convergence of a modification of the famous Weierstrass method. Afterward, we prove a semilocal convergence theorem that is of great practical importance owing to its computable initial condition. The obtained theorems improve and complement all existing such kind of convergence results about this method. At the end of the paper, we provide three numerical examples to show the applicability of our semilocal theorem to some physics problems. Within the examples, we propose a new algorithm for the experimental study of the dynamics of the simultaneous methods and compare the convergence and dynamical behaviors of the modified and the classical Weierstrass methods. Full article

Lake Taihu has highly turbid inland waters with complex optical properties. Due to the bottom effect of submerged aquatic plants in optically shallow waters, currently available phytoplankton chlorophyll-a retrieval algorithms tend to overestimate chlorophyll-a concentrations in the eastern part of Lake Taihu. This overestimation can distort the eutrophication evaluation of the entire lake. This paper identifies submerged and emergent plants, determines the retrieval models for the upwelling (K_u) and downwelling (K_d) irradiance attenuation coefficients, and proposes a phytoplankton chlorophyll-a retrieval model using a water depth optimization-based method to remove the bottom effect. The results show the following: (1) The normalized difference vegetation index (NDVI) method can distinguish the bottom mud (NDVI < −0.46) and submerged aquatic plants (−0.46 ≤ NDVI < 0.52) from the emergent plants (NDVI ≥ 0.52) with 90% accuracy. (2) The downwelling and upwelling irradiance attenuation coefficients are highly correlated with the suspended sediments, and retrieval models for these coefficients in three visible bands with high accuracy are presented. (3) Compared to traditional algorithms without bottom effect removal, the proposed chlorophyll-a concentration estimation algorithm based on the water depth-optimized bottom effect removal method efficiently reduces the bottom effect of the submerged aquatic plants. The root mean square error (RMSE) for the obtained chlorophyll-a concentrations decreases from 45.61 to 8.69 , and the mean absolute percentage error (MAPE) is reduced from 245.12% to 19.58%. In the validation step, the obtained RMSE of 10.89 and MAPE of 17.52% are consistent with the proposed algorithm. This research provides a good reference for the determination of chlorophyll-a concentrations in phytoplankton in complex inland water bodies. The findings are potentially useful for the operational monitoring of harmful algal blooms in the future. Full article

Ensuring the overall efficiency of hydraulic fracturing treatment depends on the ability to forecast bottomhole pressure. It has a direct impact on fracture geometry, production efficiency, and cost control. Since the complications present in contemporary operations have proven insufficient to overcome inherent uncertainty, the precision of bottomhole pressure predictions is of great importance. Achieving this objective is possible by employing machine learning algorithms that enable real-time forecasting of bottomhole pressure. The primary objective of this study is to produce sophisticated machine learning algorithms that can accurately predict bottomhole pressure while injecting guar cross-linked fluids into the fracture string. Using a large body of work, including 42 vertical wells, an extensive dataset was constructed and meticulously packed using processes such as feature selection and data manipulation. Eleven machine learning models were then developed using parameters typically available during hydraulic fracturing operations as input variables, including surface pressure, slurry flow rate, surface proppant concentration, tubing inside diameter, pressure gauge depth, gel load, proppant size, and specific gravity. These models were trained using actual bottomhole pressure data (measured) from deployed memory gauges. For this study, we carefully developed machine learning algorithms such as gradient boosting, AdaBoost, random forest, support vector machines, decision trees, k-nearest neighbor, linear regression, neural networks, and stochastic gradient descent. The MSE and R² values of the best-performing machine learning predictors, primarily gradient boosting, decision trees, and neural network (L-BFGS) models, demonstrate a very low MSE value and high R² correlation coefficients when mapping the predictions of bottomhole pressure to actual downhole gauge measurements. R² values are reported as 0.931, 0.903, and 0.901, and MSE values are reported at 0.003, 0.004, and 0.004, respectively. Such low MSE values together with high R² values demonstrate the exceptionally high accuracy of the developed models. By illustrating how machine learning models for predicting pressure can act as a viable alternative to expensive downhole pressure gauges and the inaccuracy of conventional models and correlations, this work provides novel insight. Additionally, machine learning models excel over traditional models because they can accommodate a diverse set of cross-linked fracture fluid systems, proppant specifications, and tubing configurations that have previously been intractable within a single conventional correlation or model. Full article