Search Results (363)

Data-driven modeling methods have been preliminarily applied in the development of tight-gas reservoirs, demonstrating unique advantages in post-fracturing productivity prediction. However, most of the established predictive models are “black-box” models, which provide productivity predictions based on a set of input parameters without revealing the internal prediction mechanisms. This lack of transparency reduces the credibility and practical utility of such models. To address the challenges of poor performance and low trustworthiness of “black-box” machine learning models, this study explores a data-driven approach to “black-box” predictive modeling by integrating ensemble learning with interpretability methods. The results indicate the following: The post-fracturing productivity prediction model for tight-gas reservoirs developed in this study, based on ensemble learning, achieves a goodness of fit of 0.923, representing a 26.09% improvement compared to the best-performing individual machine learning model. The stacking ensemble model predicts post-fracturing productivity for horizontal wells more accurately and effectively mitigates the prediction biases of individual machine learning models. An interpretability method for the “black-box” ensemble learning-based productivity prediction model was established, revealing the ranked importance of factors influencing post-fracturing productivity: reservoir properties, controllable operational parameters, and rock mechanics. This ranking aligns with the results of orthogonal experiments from mechanism-driven numerical models, providing mutual validation and enhancing the credibility of the ensemble learning-based productivity prediction model. In conclusion, this study integrates mechanistic numerical models and data-driven models to explore the influence of various factors on post-fracturing productivity. The cross-validation of results from both approaches underscores the reliability of the findings, offering theoretical and methodological support for the design of fracturing schemes and the iterative advancement of fracturing technologies in tight-gas reservoirs. Full article

The MADS-box gene family is a large family of transcription factors, and its members are widely distributed in the plant kingdom. Members of this family are well known to be crucial regulators of many biological processes and environmental responses. In this study, bioinformatics methods were employed to analyze the MADS-box gene family members in the common oat, focusing on their phylogenetic relationships, gene structures, conserved motifs, evolutionary relationships, promoter analysis and responses to photoperiod and abiotic stress. A total of 175 MADS-box genes were detected in Avena sativa, which were categorized into Type I and Type II. Type II members exhibited more complex gene structures, while each subfamily showed similar gene structures and motifs. Evolutionary analysis identified 138 segmental duplication events and revealed strong syntenic conservation with Triticum aestivum (337 collinear gene pairs). Four categories of cis-elements were detected in the promoter regions of the AsMADS-box genes. qRT-PCR analysis revealed that the expression of six Type II AsMADS-box genes varied in response to ABA, GA, drought and salt. Furthermore, 23 AsMADS-box members were potentially associated with heading date when the common oat plants were exposed to different photoperiod conditions. The overexpression of chr4D_AsMADS95 in Arabidopsis thaliana led to early flowering under long-day and short-day photoperiod conditions, likely associated with a significant increase in the expression levels of flowering-related genes in transgenic plants. These findings will provide useful information for future studies on stress responses and increase our understanding of the network that regulates flowering in the common oat. Full article

With agriculture being the second biggest contributor to greenhouse gas (GHG) emissions through the excessive use of fertilizers, machinery, and inefficient farming practices, global efforts to reduce emissions have been intensified, opting for smarter, data-driven solutions. However, while machine learning (ML) offers powerful predictive capabilities, its black-box nature presents a challenge for trust and adoption, particularly when integrated with auditable financial technology (FinTech) principles. To address this gap, this work introduces a novel, explanation-focused GHG emission optimization framework for IoT-enabled smart agriculture that is both transparent and prescriptive, distinguishing itself from macro-level land-use solutions by focusing on optimizable management practices while aligning with core FinTech principles and pollutant stock market mechanisms. The framework employs a two-stage statistical methodology that first identifies distinct agricultural emission profiles from macro-level data, and then models these emissions by developing a cluster-oriented principal component regression (PCR) model, which outperforms simpler variants by approximately 35% on average across all clusters. This interpretable model then serves as the core of a FinTech-aligned optimization framework that combines cluster-oriented modeling knowledge with a sequential least squares quadratic programming (SLSQP) algorithm to minimize emission-related costs under a carbon pricing mechanism, showcasing forecasted cost reductions as high as 43.55%. Full article

This study addresses recurring failures of a gas boiler with a steam capacity of 65,000 kg/h, which is operating in a Polish industrial plant. To determine the cause, material examinations were carried out, including chemical composition and microstructural analysis of SA178A steel, as well as strength tests. The results revealed no significant material degradation outside the cracking zones, suggesting that the failures were primarily caused by thermo-mechanical interactions. A finite element model in Ansys Workbench software was developed, incorporating thermal and mechanical boundary conditions, to reproduce the behavior of the critical section. The analysis demonstrated stress concentrations at the junction between the box and the membrane wall, resulting from large thermal displacement differences. The plastic strains under static loading do not exceed 5%, which implies that, without considering the cyclic nature of boiler operation, the wall should not experience failure. Analysis taking into account only 3 full operating cycles indicates a continuous increase in plastic deformation, which leads to the occurrence of ratcheting. To mitigate these effects, a modification of the sealing box design was proposed. Simulations indicated a reduction in plasticized zones by approximately 65%, and the effectiveness of the solution was confirmed by two years of failure-free operation. The findings highlight the importance of an integrated diagnostic, numerical, and design approach to improving boiler durability. Full article

The Cauchao berry (Amomyrtus luma), native to southern Chile and Argentina, has been traditionally used in folk medicine, yet scientific evidence supporting its bioactive potential remains limited. This study aimed to optimize the extraction of bioactive compounds and assess their antioxidant and antidiabetic properties. Fresh and freeze-dried samples were compared in terms of proximate composition, dietary fiber, reducing sugars, and fatty acid profiles. Proximate and fiber contents were determined using AOAC methods, while fatty acids were analyzed by gas chromatography, and α-tocopherol levels were measured via HPLC. Extraction optimization was conducted using a Box–Behnken design within a response surface methodology framework, employing freeze-dried samples. Total phenolic (TPC), flavonoid (TFC), and anthocyanin (TAC) contents were quantified spectrophotometrically. Antioxidant potential was assessed by DPPH and ORAC assays, while α-glucosidase inhibition determined antidiabetic activity. Phenolic profiles were characterized by HPLC. Optimal extraction conditions (58% ethanol, 60% ultrasound power, 30 min) enhanced antioxidant response. Results showed high fiber content (~39%), linoleic acid as the predominant fatty acid, and an α-tocopherol concentration of ~95 µg/g. TPC, TFC, and TAC values reached 25.43 ± 0.85, 46.51 ± 1.38, and 5.91 ± 0.40 mg/g d.m., respectively. Antioxidant capacity was 289.54 ± 9.05 μmol TE/g (DPPH) and 451.09 ± 6.04 μmol TE/g (ORAC). The IC₅₀ for α-glucosidase inhibition was 0.558 ± 0.015 mg/mL. Phenolic compounds were identified. These findings position the Cauchao berry as a promising source of bioactive compounds with potential health benefits. Full article

The Huangliu Formation, Section I, Gas Group II, at the eastern X gas field of the Yinggehai Basin, hosts thick, irregularly deposited sandstone bodies. The genesis of these sedimentary sand bodies has remained unclear. Utilizing drilling logs, core samples, and 3D seismic data from this field, this study integrates seismic geomorphology analysis, paleo-hydrodynamic reconstruction, and sedimentary numerical simulation to investigate the spatiotemporal evolution of the depositional system under micro-paleotopographic conditions during Gas Zone II sedimentation. Key conclusions include the development of seven morphologically diverse isolated sand bodies in the Lower II Gas Zone, covering areas of 1.4–13.4 km² with thicknesses ranging from 8.0 to 42.0 m. These sand bodies consist predominantly of massive fine-grained sandstone, characterized by box-shaped gamma-ray (GR) log responses and U- or V-shaped seismic reflection configurations. Reconstruction of paleo-turbidity current hydrodynamics for the Lower II depositional period was achieved through analysis of topographic slope gradients and the dimensional constraints (width/depth) of confined channels. Critically, slope gradients within the intraslope basin prompted a transition from supercritical to subcritical flow states within turbidity currents. This hydraulic transformation drove alternating erosion and deposition along the seafloor topography, ultimately generating the observed irregular, isolated turbidite sand bodies. Full article

Microseismic monitoring has become a critical technology for hydraulic fracturing in unconventional oil and gas reservoirs, owing to its high temporal and spatial resolution. It plays a pivotal role in tracking fracture propagation and evaluating stimulation effectiveness. However, the automatic picking of first-arrival times and accurate source localization remain challenging under complex noise conditions, which constrain the reliability of fracture parameter inversion and reservoir assessment. To address these limitations, we propose a hybrid approach that combines optimized variational mode decomposition (OVMD), wavelet thresholding denoising (WTD), and an adaptive fractal box-counting dimension algorithm for enhanced first-arrival picking and source localization. Specifically, OVMD is first employed to adaptively decompose seismic signals and isolate noise-dominated components. Subsequently, WTD is applied in the multi-scale frequency domain to suppress residual noise. An adaptive fractal dimension strategy is then utilized to detect change points and accurately determine the first-arrival time. These results are used as inputs to a particle swarm optimization (PSO) algorithm for source localization. Both numerical simulations and laboratory experiments demonstrate that the proposed method exhibits high robustness and localization accuracy under severe noise conditions. It significantly outperforms conventional approaches such as short-time Fourier transform (STFT) and continuous wavelet transform (CWT). The proposed framework offers reliable technical support for dynamic fracture monitoring, detailed reservoir characterization, and risk mitigation in the development of unconventional reservoirs. Full article

Complex engineering systems—such as ultra-long horizontal wells in energy exploitation and distributed rail transit infrastructure—operate under harsh physical and environmental conditions, where accurate physical modeling and real-time parameter estimation are essential for ensuring safety, efficiency, and reliability. Traditional empirical and black-box data-driven approaches often fail to account for the underlying physical mechanisms, thereby limiting interpretability and generalizability. To address this, we propose a unified framework that integrates physics-informed scenario-based modeling with data-driven parameter inversion. In the first stage, critical system parameters—such as friction coefficients in drill string movement or contact forces in rail–wheel interactions—are explicitly formulated based on mechanical theory, leveraging symmetries and boundary conditions to improve model structure and reduce computational complexity. In the second stage, model parameters are identified or updated through inverse modeling using historical or real-time field data, enhancing predictive performance and engineering insight. The proposed methodology is demonstrated through two representative cases. The first involves friction estimation during tripping operations in the SU77-XX-32H5 ultra-long horizontal well of the Sulige Gas Field, where a mechanical load model is constructed and field-calibrated. The second applies the framework to rail transit systems, where wheel–rail friction is estimated from dynamic response signals to support condition monitoring and wear prediction. The results from both scenarios confirm that incorporating physical symmetry and data-driven inversion significantly enhances the accuracy, robustness, and interpretability of engineering analyses across domains. Full article

Gas leak detection in oil and gas processing facilities is a critical component of the safety production monitoring system. Non-contact detection technology based on infrared imaging has emerged as a vital real-time monitoring method due to its rapid response and extensive coverage. However, existing pixel-level segmentation networks face challenges such as insufficient segmentation accuracy, rough gas edges, and jagged boundaries. To address these issues, this study proposes a novel pixel-level segmentation network training framework based on anchor box annotation and enhances the segmentation performance of the YOLOv8-seg network for gas detection applications. First, a dynamic threshold is introduced using the Visual Background Extractor (ViBe) method, which, in combination with the YOLOv8-det network, generates binary masks to serve as training masks. Next, a segmentation head architecture is designed, incorporating dynamic kernels and multi-branch collaboration. This architecture utilizes feature concatenation under deformable convolution and attention mechanisms to replace parts of the original segmentation head, thereby enhancing the extraction of detailed gas features and reducing dependency on anchor boxes during segmentation. Finally, a joint Dice-BCE (Binary Cross-Entropy) loss, weighted by ViBe-CRF (Conditional Random Fields) confidence, is employed to replace the original Seg_loss. This effectively reduces roughness and jaggedness at gas edges, significantly improving segmentation accuracy. Experimental results indicate that the improved network achieves a 6.4% increase in F1 score and a 7.6% improvement in the mIoU (mean Intersection over Union) metric. This advancement provides a new, real-time, and efficient detection algorithm for infrared imaging of gas leaks in oil and gas processing facilities. Furthermore, it introduces a low-cost weakly supervised learning approach for training pixel-level segmentation networks. Full article

The transportation sector is a major consumer of primary energy and is a significant contributor to greenhouse gas emissions. Sustainable transportation requires identifying and quantifying factors influencing transport-related CO₂ emissions. This research aims to establish an adaptable, precise, and transparent forecasting structure for transport CO₂ emissions of the United States. For this reason, we proposed a multi-stage method that incorporates explainable Machine Learning (ML) and Feature Selection (FS), guaranteeing interpretability in comparison to conventional black-box models. Due to high multicollinearity among 24 initial variables, hierarchical feature clustering and multi-step FS were applied, resulting in five key predictors: Total Primary Energy Imports (TPEI), Total Fossil Fuels Consumed (FFT), Annual Vehicle Miles Traveled (AVMT), Air Passengers-Domestic and International (APDI), and Unemployment Rate (UR). Four ML methods—Support Vector Regression, eXtreme Gradient Boosting, ElasticNet, and Multilayer Perceptron—were employed, with ElasticNet outperforming the others with RMSE = 45.53, MAE = 30.6, and MAPE = 0.016. SHAP analysis revealed AVMT, FFT, and APDI as the top contributors to CO₂ emissions. This framework aids policymakers in making informed decisions and setting precise investments. Full article

Background/Objective: Sepsis-associated acute kidney injury (SA-AKI) is a substantial contributor to mortality in critically ill patients. This study aimed to investigate the impact of gum acacia (GA) and dexamethasone (DEX) combination on lipopolysaccharide (LPS)-induced SA-AKI in rats. Methods: Thirty-six male Sprague Dawley rats were separated into six groups, including the control, GA group, LPS-induced AKI group, DEX + LPS group, GA + LPS group, and GA + DEX + LPS group. AKI was induced in rats using LPS (10 mg/kg, i.p.). GA was administered orally (7.5 g/kg) for 14 days before LPS injection, and DEX was injected (1 mg/kg, i.p.) 2 h after LPS injection. Results: LPS injection significantly (p < 0.05, vs. control group) impaired renal function, as evidenced through increased levels of kidney function biomarkers, decreased creatinine clearance, and histopathological alterations in the kidneys. LPS also significantly (p < 0.05, vs. control group) elevated levels of oxidative stress markers, while it reduced levels of antioxidant enzymes. Furthermore, LPS triggered an inflammatory response, manifested by significant (p < 0.05, vs. control group) upregulation of Toll-like receptor 4, myeloid differentiation primary response 88, interleukin-1β, tumor necrosis factor-α, and nuclear factor-κB, along with increased expression of high-mobility group box 1. Administration of GA significantly ameliorated LPS-induced renal impairment by enhancing antioxidant defenses and suppressing inflammatory pathways (p < 0.05, vs. LPS group). Furthermore, GA-DEX-treated rats showed improved kidney function, reduced oxidative stress, and attenuated inflammatory markers (p < 0.05, vs. LPS group). Conclusions: The GA-DEX combination exhibited potent renoprotective effects against LPS-induced SA-AKI, possibly due to their antioxidant and anti-inflammatory properties. These results suggest that the GA-DEX combination could be a promising and effective therapeutic agent for managing SA-AKI. Full article

Concern over the presence of pharmaceutical waste in the environment has prompted research into the management of emerging organic micropollutants (EOMs). In response, sustainable technologies have been applied as alternatives to reduce the effects of these contaminants. This study investigated the capacity of filamentous fungi isolated from iron mine soil in the Amazon region to biodegrade the drug chloroquine diphosphate. An initial screening assessed the growth of four fungal strains on solid media containing chloroquine diphosphate: Trichoderma pseudoasperelloides CBMAI 2752, Penicillium rolfsii CBMAI 2753, Talaromyces verruculosus CBMAI 2754, and Penicillium sp. cf. guaibinense CBMAI 2758. Among them, Penicillium sp. cf. guaibinense CBMAI 2758 was selected for further testing in liquid media. A Box–Behnken factorial design was applied with three variables, pH (5, 7, and 9), incubation time (5, 10, and 15 days), and chloroquine diphosphate concentration (50, 75, and 100 mg·L⁻¹), totaling 15 experiments. The samples were analyzed by gas chromatography–mass spectrometry (GC-MS). The most effective conditions for chloroquine biodegradation were pH 7, 100 mg·L⁻¹ concentration, and 10 days of incubation. Four metabolites were identified: one resulting from N-deethylation M1 (N4-(7-chloroquinolin-4-yl)-N1-ethylpentane-1,4-diamine), two from carbon–carbon bond cleavage M2 (7-chloro-N-ethylquinolin-4-amine) and M3 (N1,N1-diethylpentane-1,4-diamine), and one from aromatic deamination M4 (N1-ethylbutane-1,4-diamine) by enzymatic reactions. The toxicity analysis showed that the products obtained from the biodegradation of chloroquine were less toxic than the commercial formulation of this compound. These findings highlight the biotechnological potential of Amazonian fungi for drug biodegradation and decontamination. Full article

Pear (Pyrus pyrifolia) is an important deciduous fruit tree that requires a specific period of low-temperature accumulation to trigger spring flowering. The warmer winter caused by global warming has led to insufficient winter chilling, disrupting floral initiation and significantly reducing pear yields in Southern China. In this study, we integrated targeted phytohormone metabolomics, full-length transcriptomics, and proteomics to explore the regulatory mechanisms of dormancy in ‘Mixue’, a pear cultivar with an extremely low chilling requirement. Comparative analyses across the multi-omics datasets revealed 30 differentially abundant phytohormone metabolites (DPMs), 2597 differentially expressed proteins (DEPs), and 7722 differentially expressed genes (DEGs). Integrated proteomic and transcriptomic expression clustering analysis identified five members of the dormancy-associated MADS-box (DAM) gene family among dormancy-specific differentially expressed proteins (DEPs) and differentially expressed genes (DEGs). Phytohormone correlation analysis and cis-regulatory element analysis suggest that DAM genes may mediate dormancy progression by responding to abscisic acid (ABA), gibberellin (GA), and salicylic acid (SA). A dormancy-associated transcriptional regulatory network centered on DAM genes and phytohormone signaling revealed 35 transcription factors (TFs): 19 TFs appear to directly regulate the expression of DAM genes, 18 TFs are transcriptionally regulated by DAM genes, and two TFs exhibit bidirectional regulatory interactions with DAM. Within this regulatory network, we identified a novel pathway involving REVEILLE 6 (RVE6), DAM, and CONSTANS-LIKE 8 (COL8), which might play a critical role in regulating bud dormancy in the ‘Mixue’ low-chilling pear cultivar. Furthermore, lncRNAs ONT.19912.1 and ONT.20662.7 exhibit potential cis-regulatory interactions with DAM1/2/3. This study expands the DAM-mediated transcriptional regulatory network associated with bud dormancy, providing new insights into its molecular regulatory mechanisms in pear and establishing a theoretical framework for future investigations into bud dormancy control. Full article

Gallic acid, a major phenolic compound in wine, significantly influences its sensory profile and health-related properties, making its accurate measurement essential for both enological and nutritional studies. In this context, a derivatization protocol for gallic acid using ethyl chloroformate (ECF) was developed and optimized for GC-MS analysis, with experimental conditions refined through a Box–Behnken Design (BBD). The BBD systematically investigated the effects of three critical reagent volumes: ethyl chloroformate, pyridine, and ethanol. This approach elucidated complex interactions and quadratic effects, leading to a predictive second-order polynomial model and identifying the optimal derivatization conditions for maximum yield (137 µL of ethyl chloroformate, 51 µL of pyridine, and 161 µL of ethanol per 150 µL of wine). The BBD-optimized GC-MS method was validated and successfully applied to quantify gallic acid in diverse commercial wine samples (white, red, conventional, natural). A key finding was the method’s wide dynamic range, enabling accurate quantification from 5 up to over 600 µg/mL without sample dilution. This work represents, to our knowledge, the first application of a BBD for optimizing the ethyl chloroformate derivatization of gallic acid, providing a robust, efficient, and widely applicable analytical tool for routine quality control and enological research. Full article

This paper presents methods for modelling and optimising the industrial limestone grinding process carried out using a real limestone plant. Two key process evaluation indicators were developed: specific electric energy consumption and an extended indicator that also includes gas usage. Using process data collected from the SCADA system and results from industrial factorial experiments, regression artificial neural network models were developed, with controllable process parameters used as inputs. In the next phase, black-box optimisation was performed using Bayesian and genetic algorithms to identify optimal mill operating settings. The results demonstrate significant improvements in energy efficiency, with energy savings reaching up to 48% in selected cases. The proposed methodology can be effectively applied to enhance energy performance in similar industrial grinding processes. Full article