Search Results (767)

In response to the technical challenges of traditional reservoir inversion techniques in determining inter-well wavelets and estimating geological statistical parameters, this study proposes an artificial intelligence high-resolution reservoir inversion technique based on seismic waveform constraints. This technology integrates multi-source heterogeneous data such as lithology characteristics, logging curves, and seismic waveforms through a deep learning neural network framework, and constructs an intelligent reservoir prediction model with geological and physical constraints. Results demonstrate that the proposed technique significantly enhances prediction accuracy for thin sand layers by effectively extracting high-frequency seismic information and establishing robust nonlinear mapping relationships. Inversion errors of reservoir parameters were reduced by more than 25%, while a vertical resolution of 0.5 m was achieved. Predictions agreed with actual drilling data with an accuracy of 86%, representing an 18% improvement over traditional methods. In practical applications, the technique successfully supported new well placement, contributing to a 22% increase in initial oil production in the pilot area. Furthermore, this study establishes a standardized technical procedure: “Time–Depth Modeling-Phase-Controlled Interpolation-Intelligent Inversion”. This workflow provides an innovative solution for high-precision reservoir characterization in regions with limited well control and complex terrestrial depositional systems, offering both theoretical significance and practical value for advancing reservoir prediction technology. Full article

The digital economy is a key driver of industrial upgrading and regional growth. Focusing on Gansu Province—an under-represented, less-developed region in northwest China—this study constructs a multidimensional digital economy index (DEI) for 2009–2023 under a unified normalisation and weighting scheme. Two complementary MCDA approaches—entropy-weighted TOPSIS and SESP-SPOTIS—are implemented on the same 0–1 normalised indicators. Robustness is assessed using COMSAM sensitivity analysis and is benchmarked against a PCA reference. The empirical analysis then estimates log-elasticity models linking modern logistics production (MLP) and the DEI to the provincial GDP and sectoral value added, with inferences based on White heteroskedasticity–robust standard errors and bootstrap confidence intervals. Results show a steady rise in the DEI with a temporary dip in 2021 and recovery thereafter. MLP is positively and significantly associated with GDP and value added in the primary, secondary, and tertiary sectors. The DEI is positively and significantly associated with GDP, the primary sector, and the tertiary sector, but its effect is not statistically significant for the secondary sector, indicating a manufacturing digitalisation gap relative to services. Cross-method agreement and narrow sensitivity bands support the stability of these findings. Policy implications include continued investment in digital infrastructure and accessibility, targeted acceleration of manufacturing digitalisation, and the development of a “digital agriculture–smart logistics–green development” pathway to foster high-quality, sustainable regional growth. Full article

We propose a variant of Bayesian optimization in which probability distributions are constructed using uncertainty quantification (UQ) techniques. In this context, UQ techniques rely on a Hilbert basis expansion to infer probability distributions from limited experimental data. These distributions act as prior knowledge of the search space and are incorporated into the acquisition function to guide the selection of enrichment points more effectively. Several variants of the method are examined, depending on the distribution type (normal, log-normal, etc.), and benchmarked against traditional Bayesian optimization on test functions. The results show competitive performance, with selective improvements depending on the problem structure, and faster convergence in specific cases. As a practical application, we address a structural shape optimization problem. The initial geometry is an L-shaped plate, where the goal is to minimize the volume under a horizontal displacement constraint expressed as a penalty. Our approach first identifies a promising region while efficiently training the surrogate model. A subsequent gradient-based optimization step then refines the design using the trained surrogate, achieving a volume reduction of more than $30\%$ while satisfying the displacement constraint, without requiring any additional evaluations of the objective function. Full article

Background: Left ventricular myocardial work (MW) derived from non-invasive pressure–strain loops has emerged as a load-adjusted index of contractile performance. Its value for risk stratification in advanced heart failure (HF) remains uncertain. Methods: We retrospectively studied 151 consecutive patients with advanced HF undergoing comprehensive evaluation at our tertiary centre between January 2016 and December 2022. MW parameters—left ventricular global work index (LVGWI), global constructive work (LVGCW), global wasted work (LVGWW) and global work efficiency (LVGWE)—were derived from speckle-tracking echocardiography integrated with brachial blood pressure. Cardiopulmonary exercise testing (CPET), right heart catheterisation (RHC) and biochemical markers were obtained. Patients were stratified according to an LVGWI threshold of 600 mmHg%, identified by receiver operating characteristic (ROC) analysis for predicting the combined end point of cardiovascular mortality or HF hospitalisation. Correlations between MW and traditional indices were assessed, and event-free survival was analysed by Kaplan–Meier curves. Results: LVGWI correlated modestly with pVO₂ (r = 0.35, p = 0.01) and left ventricular ejection fraction (r = 0.42, p < 0.001) and inversely with NT-proBNP (r = −0.30, p = 0.03). LVGWI displayed the largest area under the curve (AUC 0.76 [95% confidence interval 0.65–0.85]) for predicting the combined end point compared with pVO₂ (AUC 0.73) and LVEF (AUC 0.67). Dichotomisation by LVGWI ≤ 600 mmHg% identified a high-risk group (Group A) with worse NYHA class, lower systolic blood pressure and reduced exercise capacity. After a median follow-up of 24 months, Group A exhibited significantly lower event-free survival (log-rank p = 0.02). Multivariable analysis was not performed owing to the limited sample size; therefore, findings should be interpreted with caution. Conclusions: In patients with advanced HF, left ventricular myocardial work, particularly LVGWI, provides incremental prognostic information beyond conventional markers. An LVGWI cut-off of 600 mmHg% derived from ROC analysis identified patients at increased risk of cardiovascular events and may inform timely referral for mechanical circulatory support or transplantation. Larger prospective studies are warranted to confirm these observations and to establish standardised thresholds across vendors. Full article

Modern construction and infrastructure projects produce large volumes of heterogeneous data, including building information models, JSON sensor streams, and maintenance logs. Ensuring interoperability and data integrity across diverse software platforms requires standardized data exchange methods. However, traditional neutral object models, often constrained by rigid and incompatible schemas, are ill-suited to accommodate the heterogeneity and long-term nature of such data. Addressing this challenge, the study proposes a schema-less data exchange approach that improves flexibility in representing and interpreting infrastructure information. The method uses dynamic JSON-based objects, with infrastructure model definitions serving as semantic guidelines rather than rigid templates. Rule-based reasoning and dictionary-guided term mapping are employed to infer entity types from semi-structured data without enforcing prior schema conformance. Experimental evaluation across four datasets demonstrated exact entity-type match rates ranging from 61.4% to 76.5%, with overall success rates—including supertypes and ties—reaching up to 95.0% when weighted accuracy metrics were applied. Compared to a previous baseline, the method showed a notable improvement in exact matches while maintaining overall performance. These results confirm the feasibility of schema-less inference using domain dictionaries and indicate that incorporating schema-derived constraints could further improve accuracy and applicability in real-world infrastructure data environments. Full article

This study addresses the nonlinear estimation problem in the strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation by proposing an improved filtering algorithm based on $S{E}_2(3)$ Lie group state representation. A dynamic model satisfying the group affine condition is established to systematically construct both left-invariant and right-invariant error state spaces, upon which two nonlinear filtering approaches are developed. Although the fast unscented transformation method is not novel by itself, its first integration with the $S{E}_2(3)$ Lie group model for SINS/DVL integrated navigation represents a significant advancement. Experimental results demonstrate that under large misalignment angles, the proposed method achieves slightly lower attitude errors compared to linear approaches, while also reducing position estimation errors during dynamic maneuvers. The 12,000 s endurance test confirms the algorithm’s stable long-term performance. Compared with conventional unscented Kalman filter methods, the proposed approach not only reduces computation time by 90% but also achieves real-time processing capability on embedded platforms through optimized sampling strategies and hierarchical state propagation mechanisms. These innovations provide an underwater navigation solution that combines theoretical rigor with engineering practicality, effectively overcoming the computational efficiency and dynamic adaptability limitations of traditional nonlinear filtering methods. Full article

The aim of this study was to evaluate the treatment efficiency and applicability of using textile-based mats as roof biofilters on urban buildings for purifying preliminary treated wastewater (PTW) collected from a three-chamber septic tank. Therefore, a pilot plant with a 15° pitched wooden roof and two tracks for laying two mats made of different materials—polypropylene (PP), designated as Mat 1, and polyethylene terephthalate (PET), designated as Mat 2—was constructed at ground level under outdoor conditions. The plant was operated in parallel for a period of 455 days. Significant differences (p < 0.05) were observed in the results of the mass removal efficiencies between the two mats, with Mat 1 achieving mean removals of five-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), ammonium-nitrogen (NH₄-N), and total nitrogen (TN) of 85%, 73%, 75%, and 38%, respectively, and Mat 2 achieving comparatively higher removals of 97%, 84%, 90%, and 57%, respectively. The mean concentrations of BOD₅ and COD at the outflow of both mats met the minimum water quality requirements for discharge and successfully met the minimum water quality class B for agricultural reuse. However, the comparatively low mean E. coli removal efficiencies of 2.0 and 2.4 log-units in Mat 1 and Mat 2, respectively, demonstrate the need for an effluent disinfection system. Highly efficient mass removal efficiencies were observed in the presence of dense vegetation on the mats, which may lead to a potential improvement in the urban climate through high daily evapotranspiration. Overall, this study demonstrates the potential for using lightweight, textile-based mats on rooftops to efficiently treat PTW from urban buildings, offering a promising decentralized wastewater management approach for climate-resilient cities. Full article

Optimizing metadata indexing remains critical for enhancing distributed file system performance. The Traditional Log-Structured Merge-Trees (LSM-Trees) architecture, while effective for write-intensive operations, exhibits significant limitations when handling massive metadata workloads, particularly manifesting as suboptimal read performance and substantial indexing overhead. Although existing learned indexes perform well on read-only workloads, they struggle to support modifications such as inserts and updates effectively. This paper proposes SwiftKV, a novel metadata indexing scheme that combines LSM-Tree and learned indexes to address these issues. Firstly, SwiftKV employs a dynamic partition strategy to narrow the metadata search range. Secondly, a two-level learned index block, consisting of Greedy Piecewise Linear Regression (Greedy-PLR) and Linear Regression (LR) models, is leveraged to replace the typical Sorted String Table (SSTable) index block for faster location prediction than binary search. Thirdly, SwiftKV incorporates a load-aware construction mechanism and parallel optimization to minimize training overhead and enhance efficiency. This work bridges the gap between LSM-Trees’ write efficiency and learned indexes’ query performance, offering a scalable and high-performance solution for modern distributed file systems. This paper implements the prototype of SwiftKV based on RocksDB. The experimental results show that it narrows the memory usage of index blocks by 30.06% and reduces read latency by 1.19×~1.60× without affecting write performance. Furthermore, SwiftKV’s two-level learned index achieves a 15.13% reduction in query latency and a 44.03% reduction in memory overhead compared to a single-level model. For all YCSB workloads, SwiftKV outperforms other schemes. Full article

This study presents an integrated geomechanical modeling framework for predicting multi-scale fracture networks and their activity in the Longmaxi Formation shale reservoir, northern Luzhou region, southeastern Sichuan Basin—an area shaped by complex, multi-phase tectonic deformation that poses significant challenges for resource prospecting. The workflow begins with quantitative characterization of key mechanical parameters, including uniaxial compressive strength, Young’s modulus, Poisson’s ratio, and tensile strength, obtained from core experiments and log-based inversion. These parameters form the foundation for multi-phase finite element simulations that reconstruct paleo- and present-day stress fields associated with the Indosinian (NW–SE compression), Yanshanian (NWW–SEE compression), and Himalayan (near W–E compression) deformation phases. Optimized Mohr–Coulomb and tensile failure criteria, coupled with a multi-phase stress superposition algorithm, enable quantitative prediction of fracture density, aperture, and orientation through successive tectonic cycles. The results reveal that the Longmaxi Formation’s high brittleness and lithological heterogeneity interact with evolving stress regimes to produce fracture systems that are strongly anisotropic and phase-dependent: initial NE–SW-oriented domains established during the Indosinian phase were intensified during Yanshanian reactivation, while Himalayan uplift induced regional stress attenuation with limited new fracture formation. The cumulative stress effects yield fracture networks concentrated along NE–SW fold axes, fault zones, and intersection zones. By integrating geomechanical predictions with seismic attributes and borehole observations, the study constructs a discrete fracture network that captures both large-scale tectonic fractures and small-scale features beyond seismic resolution. Fracture activity is further assessed using friction coefficient analysis, delineating zones of high activity along fold–fault intersections and stress concentration areas. This principle-driven approach demonstrates how mechanical characterization, stress field evolution, and fracture mechanics can be combined into a unified predictive tool, offering a transferable methodology for structurally complex, multi-deformation reservoirs. Beyond its relevance to shale gas development, the framework exemplifies how advanced geomechanical modeling can enhance resource prospecting efficiency and accuracy in diverse geological settings. Full article

The abnormal pore pressures in ultra-deep wells in the Junggar Basin, China are constantly causing drilling incidents for both the drilling engineers and geologists. Formation pore-pressure is an important parameter in wellbore stability analysis, and accurate prediction of pore pressure before drilling is of great significance for effectively controlling wellbore instability. In this paper, the authors utilize seismic velocity inversion and rock mechanics prediction to evaluate the three pressure parameters, i.e., pore pressure, collapse pressure, and fracture pressure. Seismic data were inversed and the velocity model was constructed. Then, the layering models of the relationships between seismic velocity and logging data of the whole formation layers were constructed using seismic attributes and the corresponding acoustic logging data. Finally, the acoustic logging data, or interval transit time of ten corresponding formations, were predicted using layering models of seismic data. In an ultra-deep well, two abnormal highly pressurized sections were confirmed. This shows great potential for realizing real-time prediction of acoustic and density log data of undrilled formations in this area. Field applications confirm that the proposed method enhances prediction accuracy and computational efficiency compared to the Eston method. Two abnormal high-pressure zones were successfully identified in the Huqian-1 well, i.e., the Taxihe Formation (1.38 g/cm³) and the Anjihaihe Formation (1.50 g/cm³). Full article

The G Block of Daqing Oilfield is a crucial area for sustainable development and stable production. In addressing the technical bottlenecks of high-resolution logging data interpretation for reservoir evaluation in the Block, this study proposes a resistivity curve prediction method based on machine learning algorithms. Traditional interpretation models relying on DLS logging data face two major challenges when applied to 0.2 m high-resolution logging: first, the interpreted effective thickness of the reservoir tends to be overestimated, and second, the accuracy of fluid property identification declines. Additionally, the lack of corresponding well-test data for new logging datasets further constrains the development of interpretation models. To tackle these challenges, this study employs the XGBoost algorithm to construct a high-precision resistivity prediction model. Through systematic analysis of various logging parameter combinations, the optimal feature set comprising HAC, MSFL, and GR curves was identified. Training and testing results demonstrate that the model achieves a mean absolute error (MAE) of 0.94 Ω·m and a root mean square error (RMSE) of 1.79 Ω·m in predicting resistivity. After optimization, the model’s performance improved significantly, with MAE and RMSE reduced to 0.75 Ω·m and 1.31 Ω·m, respectively. To evaluate the model’s reliability, an external validation test was conducted on Well GFX2, yielding MAE and RMSE values of 0.91 Ω·m and 1.43 Ω·m, confirming the model’s strong generalization capability. Furthermore, the RLLD-AC and RLLD-DEN crossplots constructed from the predicted results exhibit excellent fluid identification performance in practical applications, achieving an accuracy rate exceeding 89%, which aligns well with production test data. The findings of this study provide new technical support for fine reservoir characterization in the study area and offer significant practical guidance for development plan adjustments. Full article

Measuring the bioeconomy enables policymakers to monitor advancements in sustainable development goals, identify growth opportunities, comprehend the economic implications of bio-based products, assess environmental impacts, and shape policies that foster a sustainable economy reliant on renewable biological resources. For this purpose, this study measures the value of the bioeconomy in New Zealand using the latest published input–output table for the year 2020. This study estimates the size and economic significance of New Zealand’s bioeconomy by applying two complementary methodologies. Results indicate that, in 2020, the total value added by the bioeconomy ranged from NZD 48.8 billion to NZD 50.8 billion, representing 16.5% to 17.1% of the nation’s total value added. Agriculture emerged as the dominant contributor, accounting for approximately 89% of the sector’s total value added, followed by forestry and logging at around 11%. To identify potential growth areas, the analysis further disaggregated bioeconomy value added by economic subsectors. Among bio-based industries, food manufacturing was the largest contributor, generating 43.1% (NZD 21 billion) of total bioeconomy value added, followed by bio-based services at 12.9% (NZD 6.3 billion). The biotechnology sector contributed NZD 0.34 billion, equivalent to 0.7% of the total bioeconomy. Additional significant contributors included wood processing and manufacturing (3.3%; NZD 1.6 billion), construction (0.71%; NZD 0.35 billion), and textiles and clothing (0.58%; NZD 0.29 billion). These findings underscore the pivotal role of food manufacturing, services, wood processing, textiles and clothing, and construction in shaping the bioeconomy. They further highlight the importance of assessing the economic and environmental impacts of bio-based industries and formulating policy frameworks that support a sustainable, renewable resource-based economy. Full article

Developmental dysplasia of the hip (DDH) is a congenital disorder influenced by genetic and epigenetic factors. This study aimed to elucidate the molecular pathogenesis of DDH through a comprehensive transcriptomic analysis, identifying differentially expressed genes (DEGs) and long non-coding RNAs (lncRNAs) in hip joint capsules from DDH patients and healthy controls. RNA sequencing data from 12 samples (6 DDH, 6 controls) were retrieved from the NCBI database. Functional annotation was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses via the DAVID tool. A protein–protein interaction (PPI) network of DEGs was constructed using STRING with medium confidence settings. Among 78,930 transcripts, 4.3% were significantly differentially expressed, according to DESeq2 analysis. A total of 3425 DEGs were identified (FDR < 0.05, |log2 FC| > 2), including 1008 upregulated and 2417 downregulated transcripts in DDH samples. Additionally, 1656 lncRNAs were detected among the DEGs. These findings enhance our understanding of the genetic and epigenetic landscape of DDH and highlight the involvement of key biological pathways such as cell cycle regulation and Wnt signaling. This study provides a foundation for future molecular research into the pathogenesis of DDH. Full article

Machine learning-guided predictive models are attractive in rock modeling for different scholars to obtain continuous profiles of rock compressive strength in rock engineering. The major objectives of the study are to assess the implications of machine learning (ML)-based connectionist models to obtain the unconfined compressive strength (UCS) of rock, to perform parametric sensitivity analysis on petrophysical parameters, and to develop an improved correlation for UCS prediction. The least-squares support vector machine (LSSVM) is applied to develop data-driven models for the prediction of UCS. Additionally, the random forest (RF) algorithm is applied to verify the effectiveness of predictive models. A database containing well-logging data is processed and utilized to construct connectionist models to obtain UCS. For the efficacy of predictive models, statistical performance indicators such as the coefficient of determination (CC), average percentage relative error, and maximum average percentage error are utilized in the study. It is revealed that the RF- and LSSVM-based models for predicting UCS perform excellently with high precision. Considering the parametric sensitivity analysis in the predictive models for UCS, the formation compressional wave velocity and formation gamma-ray are the most strongly contributing predictor variables rather than other input variables such as the modulus of elasticity, acoustic shear wave velocity, and rock bulk density. The improved correlation for predicting UCS shows high precision, achieving a CC of 96% and root mean squared error of 0.54 MPa. This systematic research workflow is significant and can be utilized for connectionist robust model development and variable selections in the petroleum and mining fields, such as predicting reservoir properties, the drilling rate of penetration, sanding potentiality of hydrocarbon reservoir rocks, and for the practical implications of boring and geotechnical engineering projects. Full article

Due to their low permeability, the location of natural fractures is key to the successful development of buried hill reservoirs. Due to the high degree of rock fragmentation and strong absorption of seismic waves at the top of buried hill formations, it is hard to identify the distribution of natural fractures inside a buried hill using conventional seismic methods. To overcome this difficulty, this study proposes a natural fracture identification technology for buried hill reservoirs that combines a continuum damage mechanics model with finite element numerical simulation. A 3D numerical solution workflow is established to determine the natural fracture distribution in target buried hill reservoirs. By constructing a geological model of a block, reconstructing the orogenic history, developing a 3D finite element model, and performing numerical simulations, the multi-stage orogenic processes experienced by buried hill reservoirs and the resultant natural fracture formation are replicated. This approach yields 3D numerical results of natural fracture distribution. Using the G-Block in the Zhongyuan Oilfield as a case study, the natural fracture distribution in a buried hill reservoir composed of mixed lithologies, including marble and Carboniferous formations, within the faulted G6-well group is analyzed. The results include plane views of the contour of damage variable SDEG, which represents the fracture distribution within the subsurface layer at 600 m intervals below the buried hill surface, as well as a vertical sectional view of the contour of SDEG’s distribution along specified well trajectories. By comparison with the results of the fracture distribution obtained with logging data, a consistency of 87.5% is achieved. This indicates the reliability of the numerical results for natural fractures obtained using the technology presented here. Full article